Jet energy measurement and its systematic uncertainty in proton–proton collisions at \(\sqrt{s}=7\) TeV with the ATLAS detector

  • Atlas Collaboration
  • G. Aad
  • T. Abajyan
  • B. Abbott
  • J. Abdallah
  • S. Abdel Khalek
  • O. Abdinov
  • R. Aben
  • B. Abi
  • M. Abolins
  • O. S. AbouZeid
  • H. Abramowicz
  • H. Abreu
  • Y. Abulaiti
  • B. S. Acharya
  • L. Adamczyk
  • D. L. Adams
  • T. N. Addy
  • J. Adelman
  • S. Adomeit
  • T. Adye
  • S. Aefsky
  • T. Agatonovic-Jovin
  • J. A. Aguilar-Saavedra
  • M. Agustoni
  • S. P. Ahlen
  • A. Ahmad
  • F. Ahmadov
  • G. Aielli
  • T. P. A. Åkesson
  • G. Akimoto
  • A. V. Akimov
  • M. A. Alam
  • J. Albert
  • S. Albrand
  • M. J. Alconada Verzini
  • M. Aleksa
  • I. N. Aleksandrov
  • F. Alessandria
  • C. Alexa
  • G. Alexander
  • G. Alexandre
  • T. Alexopoulos
  • M. Alhroob
  • M. Aliev
  • G. Alimonti
  • L. Alio
  • J. Alison
  • B. M. M. Allbrooke
  • L. J. Allison
  • P. P. Allport
  • S. E. Allwood-Spiers
  • J. Almond
  • A. Aloisio
  • R. Alon
  • A. Alonso
  • F. Alonso
  • A. Altheimer
  • B. Alvarez Gonzalez
  • M. G. Alviggi
  • K. Amako
  • Y. Amaral Coutinho
  • C. Amelung
  • V. V. Ammosov
  • S. P. Amor Dos Santos
  • A. Amorim
  • S. Amoroso
  • N. Amram
  • G. Amundsen
  • C. Anastopoulos
  • L. S. Ancu
  • N. Andari
  • T. Andeen
  • C. F. Anders
  • G. Anders
  • K. J. Anderson
  • A. Andreazza
  • V. Andrei
  • X. S. Anduaga
  • S. Angelidakis
  • P. Anger
  • A. Angerami
  • F. Anghinolfi
  • A. V. Anisenkov
  • N. Anjos
  • A. Annovi
  • A. Antonaki
  • M. Antonelli
  • A. Antonov
  • J. Antos
  • F. Anulli
  • M. Aoki
  • L. Aperio Bella
  • R. Apolle
  • G. Arabidze
  • I. Aracena
  • Y. Arai
  • A. T. H. Arce
  • S. Arfaoui
  • J-F. Arguin
  • S. Argyropoulos
  • E. Arik
  • M. Arik
  • A. J. Armbruster
  • O. Arnaez
  • V. Arnal
  • O. Arslan
  • A. Artamonov
  • G. Artoni
  • S. Asai
  • N. Asbah
  • S. Ask
  • B. Åsman
  • L. Asquith
  • K. Assamagan
  • R. Astalos
  • A. Astbury
  • M. Atkinson
  • N. B. Atlay
  • B. Auerbach
  • E. Auge
  • K. Augsten
  • M. Aurousseau
  • G. Avolio
  • G. Azuelos
  • Y. Azuma
  • M. A. Baak
  • C. Bacci
  • A. M. Bach
  • H. Bachacou
  • K. Bachas
  • M. Backes
  • M. Backhaus
  • J. Backus Mayes
  • E. Badescu
  • P. Bagiacchi
  • P. Bagnaia
  • Y. Bai
  • D. C. Bailey
  • T. Bain
  • J. T. Baines
  • O. K. Baker
  • S. Baker
  • P. Balek
  • F. Balli
  • E. Banas
  • Sw. Banerjee
  • D. Banfi
  • A. Bangert
  • V. Bansal
  • H. S. Bansil
  • L. Barak
  • S. P. Baranov
  • T. Barber
  • E. L. Barberio
  • D. Barberis
  • M. Barbero
  • T. Barillari
  • M. Barisonzi
  • T. Barklow
  • N. Barlow
  • B. M. Barnett
  • R. M. Barnett
  • A. Baroncelli
  • G. Barone
  • A. J. Barr
  • F. Barreiro
  • J. Barreiro Guimarães da Costa
  • R. Bartoldus
  • A. E. Barton
  • P. Bartos
  • V. Bartsch
  • A. Bassalat
  • A. Basye
  • R. L. Bates
  • L. Batkova
  • J. R. Batley
  • M. Battistin
  • F. Bauer
  • H. S. Bawa
  • T. Beau
  • P. H. Beauchemin
  • R. Beccherle
  • P. Bechtle
  • H. P. Beck
  • K. Becker
  • S. Becker
  • M. Beckingham
  • A. J. Beddall
  • A. Beddall
  • S. Bedikian
  • V. A. Bednyakov
  • C. P. Bee
  • L. J. Beemster
  • T. A. Beermann
  • M. Begel
  • K. Behr
  • C. Belanger-Champagne
  • P. J. Bell
  • W. H. Bell
  • G. Bella
  • L. Bellagamba
  • A. Bellerive
  • M. Bellomo
  • A. Belloni
  • O. L. Beloborodova
  • K. Belotskiy
  • O. Beltramello
  • O. Benary
  • D. Benchekroun
  • K. Bendtz
  • N. Benekos
  • Y. Benhammou
  • E. Benhar Noccioli
  • J. A. Benitez Garcia
  • D. P. Benjamin
  • J. R. Bensinger
  • K. Benslama
  • S. Bentvelsen
  • D. Berge
  • E. Bergeaas Kuutmann
  • N. Berger
  • F. Berghaus
  • E. Berglund
  • J. Beringer
  • C. Bernard
  • P. Bernat
  • R. Bernhard
  • C. Bernius
  • F. U. Bernlochner
  • T. Berry
  • P. Berta
  • C. Bertella
  • F. Bertolucci
  • M. I. Besana
  • G. J. Besjes
  • O. Bessidskaia
  • N. Besson
  • S. Bethke
  • W. Bhimji
  • R. M. Bianchi
  • L. Bianchini
  • M. Bianco
  • O. Biebel
  • S. P. Bieniek
  • K. Bierwagen
  • J. Biesiada
  • M. Biglietti
  • J. Bilbao De Mendizabal
  • H. Bilokon
  • M. Bindi
  • S. Binet
  • A. Bingul
  • C. Bini
  • B. Bittner
  • C. W. Black
  • J. E. Black
  • K. M. Black
  • D. Blackburn
  • R. E. Blair
  • J.-B. Blanchard
  • T. Blazek
  • I. Bloch
  • C. Blocker
  • J. Blocki
  • W. Blum
  • U. Blumenschein
  • G. J. Bobbink
  • V. S. Bobrovnikov
  • S. S. Bocchetta
  • A. Bocci
  • C. R. Boddy
  • M. Boehler
  • J. Boek
  • T. T. Boek
  • N. Boelaert
  • J. A. Bogaerts
  • A. G. Bogdanchikov
  • A. Bogouch
  • C. Bohm
  • J. Bohm
  • V. Boisvert
  • T. Bold
  • V. Boldea
  • A. S. Boldyrev
  • N. M. Bolnet
  • M. Bomben
  • M. Bona
  • M. Boonekamp
  • S. Bordoni
  • C. Borer
  • A. Borisov
  • G. Borissov
  • M. Borri
  • S. Borroni
  • J. Bortfeldt
  • V. Bortolotto
  • K. Bos
  • D. Boscherini
  • M. Bosman
  • H. Boterenbrood
  • J. Bouchami
  • J. Boudreau
  • E. V. Bouhova-Thacker
  • D. Boumediene
  • C. Bourdarios
  • N. Bousson
  • S. Boutouil
  • A. Boveia
  • J. Boyd
  • I. R. Boyko
  • I. Bozovic-Jelisavcic
  • J. Bracinik
  • P. Branchini
  • A. Brandt
  • G. Brandt
  • O. Brandt
  • U. Bratzler
  • B. Brau
  • J. E. Brau
  • H. M. Braun
  • S. F. Brazzale
  • B. Brelier
  • K. Brendlinger
  • R. Brenner
  • S. Bressler
  • T. M. Bristow
  • D. Britton
  • F. M. Brochu
  • I. Brock
  • R. Brock
  • F. Broggi
  • C. Bromberg
  • J. Bronner
  • G. Brooijmans
  • T. Brooks
  • W. K. Brooks
  • J. Brosamer
  • E. Brost
  • G. Brown
  • J. Brown
  • P. A. Bruckman de Renstrom
  • D. Bruncko
  • R. Bruneliere
  • S. Brunet
  • A. Bruni
  • G. Bruni
  • M. Bruschi
  • L. Bryngemark
  • T. Buanes
  • Q. Buat
  • F. Bucci
  • P. Buchholz
  • R. M. Buckingham
  • A. G. Buckley
  • S. I. Buda
  • I. A. Budagov
  • B. Budick
  • F. Buehrer
  • L. Bugge
  • M. K. Bugge
  • O. Bulekov
  • A. C. Bundock
  • M. Bunse
  • H. Burckhart
  • S. Burdin
  • T. Burgess
  • B. Burghgrave
  • S. Burke
  • I. Burmeister
  • E. Busato
  • V. Büscher
  • P. Bussey
  • C. P. Buszello
  • B. Butler
  • J. M. Butler
  • A. I. Butt
  • C. M. Buttar
  • J. M. Butterworth
  • W. Buttinger
  • A. Buzatu
  • M. Byszewski
  • S. Cabrera Urbán
  • D. Caforio
  • O. Cakir
  • P. Calafiura
  • G. Calderini
  • P. Calfayan
  • R. Calkins
  • L. P. Caloba
  • R. Caloi
  • D. Calvet
  • S. Calvet
  • R. Camacho Toro
  • P. Camarri
  • D. Cameron
  • L. M. Caminada
  • R. Caminal Armadans
  • S. Campana
  • M. Campanelli
  • V. Canale
  • F. Canelli
  • A. Canepa
  • J. Cantero
  • R. Cantrill
  • T. Cao
  • M. D. M. Capeans Garrido
  • I. Caprini
  • M. Caprini
  • M. Capua
  • R. Caputo
  • R. Cardarelli
  • T. Carli
  • G. Carlino
  • L. Carminati
  • S. Caron
  • E. Carquin
  • G. D. Carrillo-Montoya
  • A. A. Carter
  • J. R. Carter
  • J. Carvalho
  • D. Casadei
  • M. P. Casado
  • C. Caso
  • E. Castaneda-Miranda
  • A. Castelli
  • V. Castillo Gimenez
  • N. F. Castro
  • P. Catastini
  • A. Catinaccio
  • J. R. Catmore
  • A. Cattai
  • G. Cattani
  • S. Caughron
  • V. Cavaliere
  • D. Cavalli
  • M. Cavalli-Sforza
  • V. Cavasinni
  • F. Ceradini
  • B. Cerio
  • K. Cerny
  • A. S. Cerqueira
  • A. Cerri
  • L. Cerrito
  • F. Cerutti
  • A. Cervelli
  • S. A. Cetin
  • A. Chafaq
  • D. Chakraborty
  • I. Chalupkova
  • K. Chan
  • P. Chang
  • B. Chapleau
  • J. D. Chapman
  • D. Charfeddine
  • D. G. Charlton
  • V. Chavda
  • C. A. Chavez Barajas
  • S. Cheatham
  • S. Chekanov
  • S. V. Chekulaev
  • G. A. Chelkov
  • M. A. Chelstowska
  • C. Chen
  • H. Chen
  • K. Chen
  • L. Chen
  • S. Chen
  • X. Chen
  • Y. Chen
  • Y. Cheng
  • A. Cheplakov
  • R. Cherkaoui El Moursli
  • V. Chernyatin
  • E. Cheu
  • L. Chevalier
  • V. Chiarella
  • G. Chiefari
  • J. T. Childers
  • A. Chilingarov
  • G. Chiodini
  • A. S. Chisholm
  • R. T. Chislett
  • A. Chitan
  • M. V. Chizhov
  • S. Chouridou
  • B. K. B. Chow
  • I. A. Christidi
  • D. Chromek-Burckhart
  • M. L. Chu
  • J. Chudoba
  • G. Ciapetti
  • A. K. Ciftci
  • R. Ciftci
  • D. Cinca
  • V. Cindro
  • A. Ciocio
  • M. Cirilli
  • P. Cirkovic
  • Z. H. Citron
  • M. Citterio
  • M. Ciubancan
  • A. Clark
  • P. J. Clark
  • R. N. Clarke
  • W. Cleland
  • J. C. Clemens
  • B. Clement
  • C. Clement
  • Y. Coadou
  • M. Cobal
  • A. Coccaro
  • J. Cochran
  • S. Coelli
  • L. Coffey
  • J. G. Cogan
  • J. Coggeshall
  • J. Colas
  • B. Cole
  • S. Cole
  • A. P. Colijn
  • C. Collins-Tooth
  • J. Collot
  • T. Colombo
  • G. Colon
  • G. Compostella
  • P. Conde Muiño
  • E. Coniavitis
  • M. C. Conidi
  • I. A. Connelly
  • S. M. Consonni
  • V. Consorti
  • S. Constantinescu
  • C. Conta
  • G. Conti
  • F. Conventi
  • M. Cooke
  • B. D. Cooper
  • A. M. Cooper-Sarkar
  • N. J. Cooper-Smith
  • K. Copic
  • T. Cornelissen
  • M. Corradi
  • F. Corriveau
  • A. Corso-Radu
  • A. Cortes-Gonzalez
  • G. Cortiana
  • G. Costa
  • M. J. Costa
  • D. Costanzo
  • D. Côté
  • G. Cottin
  • L. Courneyea
  • G. Cowan
  • B. E. Cox
  • K. Cranmer
  • G. Cree
  • S. Crépé-Renaudin
  • F. Crescioli
  • M. Crispin Ortuzar
  • M. Cristinziani
  • G. Crosetti
  • C.-M. Cuciuc
  • C. Cuenca Almenar
  • T. Cuhadar Donszelmann
  • J. Cummings
  • M. Curatolo
  • C. Cuthbert
  • H. Czirr
  • P. Czodrowski
  • Z. Czyczula
  • S. D’Auria
  • M. D’Onofrio
  • A. D’Orazio
  • M. J. Da Cunha Sargedas De Sousa
  • C. Da Via
  • W. Dabrowski
  • A. Dafinca
  • T. Dai
  • F. Dallaire
  • C. Dallapiccola
  • M. Dam
  • A. C. Daniells
  • M. Dano Hoffmann
  • V. Dao
  • G. Darbo
  • G. L. Darlea
  • S. Darmora
  • J. A. Dassoulas
  • W. Davey
  • C. David
  • T. Davidek
  • E. Davies
  • M. Davies
  • O. Davignon
  • A. R. Davison
  • Y. Davygora
  • E. Dawe
  • I. Dawson
  • R. K. Daya-Ishmukhametova
  • K. De
  • R. de Asmundis
  • S. De Castro
  • S. De Cecco
  • J. de Graat
  • N. De Groot
  • P. de Jong
  • C. De La Taille
  • H. De la Torre
  • F. De Lorenzi
  • L. De Nooij
  • D. De Pedis
  • A. De Salvo
  • U. De Sanctis
  • A. De Santo
  • J. B. De Vivie De Regie
  • G. De Zorzi
  • W. J. Dearnaley
  • R. Debbe
  • C. Debenedetti
  • B. Dechenaux
  • D. V. Dedovich
  • J. Degenhardt
  • J. Del Peso
  • T. Del Prete
  • T. Delemontex
  • F. Deliot
  • M. Deliyergiyev
  • A. Dell’Acqua
  • L. Dell’Asta
  • M. Della Pietra
  • D. della Volpe
  • M. Delmastro
  • P. A. Delsart
  • C. Deluca
  • S. Demers
  • M. Demichev
  • A. Demilly
  • B. Demirkoz
  • S. P. Denisov
  • D. Derendarz
  • J. E. Derkaoui
  • F. Derue
  • P. Dervan
  • K. Desch
  • P. O. Deviveiros
  • A. Dewhurst
  • B. DeWilde
  • S. Dhaliwal
  • R. Dhullipudi
  • A. Di Ciaccio
  • L. Di Ciaccio
  • A. Di Domenico
  • C. Di Donato
  • A. Di Girolamo
  • B. Di Girolamo
  • A. Di Mattia
  • B. Di Micco
  • R. Di Nardo
  • A. Di Simone
  • R. Di Sipio
  • D. Di Valentino
  • M. A. Diaz
  • E. B. Diehl
  • J. Dietrich
  • T. A. Dietzsch
  • S. Diglio
  • K. Dindar Yagci
  • J. Dingfelder
  • C. Dionisi
  • P. Dita
  • S. Dita
  • F. Dittus
  • F. Djama
  • T. Djobava
  • M. A. B. do Vale
  • A. Do Valle Wemans
  • T. K. O. Doan
  • D. Dobos
  • E. Dobson
  • J. Dodd
  • C. Doglioni
  • T. Doherty
  • T. Dohmae
  • J. Dolejsi
  • Z. Dolezal
  • B. A. Dolgoshein
  • M. Donadelli
  • S. Donati
  • P. Dondero
  • J. Donini
  • J. Dopke
  • A. Doria
  • A. Dos Anjos
  • A. Dotti
  • M. T. Dova
  • A. T. Doyle
  • M. Dris
  • J. Dubbert
  • S. Dube
  • E. Dubreuil
  • E. Duchovni
  • G. Duckeck
  • O. A. Ducu
  • D. Duda
  • A. Dudarev
  • F. Dudziak
  • L. Duflot
  • L. Duguid
  • M. Dührssen
  • M. Dunford
  • H. Duran Yildiz
  • M. Düren
  • M. Dwuznik
  • J. Ebke
  • W. Edson
  • C. A. Edwards
  • N. C. Edwards
  • W. Ehrenfeld
  • T. Eifert
  • G. Eigen
  • K. Einsweiler
  • E. Eisenhandler
  • T. Ekelof
  • M. El Kacimi
  • M. Ellert
  • S. Elles
  • F. Ellinghaus
  • K. Ellis
  • N. Ellis
  • J. Elmsheuser
  • M. Elsing
  • D. Emeliyanov
  • Y. Enari
  • O. C. Endner
  • M. Endo
  • R. Engelmann
  • J. Erdmann
  • A. Ereditato
  • D. Eriksson
  • G. Ernis
  • J. Ernst
  • M. Ernst
  • J. Ernwein
  • D. Errede
  • S. Errede
  • E. Ertel
  • M. Escalier
  • H. Esch
  • C. Escobar
  • X. Espinal Curull
  • B. Esposito
  • F. Etienne
  • A. I. Etienvre
  • E. Etzion
  • D. Evangelakou
  • H. Evans
  • L. Fabbri
  • G. Facini
  • R. M. Fakhrutdinov
  • S. Falciano
  • Y. Fang
  • M. Fanti
  • A. Farbin
  • A. Farilla
  • T. Farooque
  • S. Farrell
  • S. M. Farrington
  • P. Farthouat
  • F. Fassi
  • P. Fassnacht
  • D. Fassouliotis
  • B. Fatholahzadeh
  • A. Favareto
  • L. Fayard
  • P. Federic
  • O. L. Fedin
  • W. Fedorko
  • M. Fehling-Kaschek
  • L. Feligioni
  • C. Feng
  • E. J. Feng
  • H. Feng
  • A. B. Fenyuk
  • W. Fernando
  • S. Ferrag
  • J. Ferrando
  • V. Ferrara
  • A. Ferrari
  • P. Ferrari
  • R. Ferrari
  • D. E. Ferreira de Lima
  • A. Ferrer
  • D. Ferrere
  • C. Ferretti
  • A. Ferretto Parodi
  • M. Fiascaris
  • F. Fiedler
  • A. Filipčič
  • M. Filipuzzi
  • F. Filthaut
  • M. Fincke-Keeler
  • K. D. Finelli
  • M. C. N. Fiolhais
  • L. Fiorini
  • A. Firan
  • J. Fischer
  • M. J. Fisher
  • E. A. Fitzgerald
  • M. Flechl
  • I. Fleck
  • P. Fleischmann
  • S. Fleischmann
  • G. T. Fletcher
  • G. Fletcher
  • T. Flick
  • A. Floderus
  • L. R. Flores Castillo
  • A. C. Florez Bustos
  • M. J. Flowerdew
  • T. Fonseca Martin
  • A. Formica
  • A. Forti
  • D. Fortin
  • D. Fournier
  • H. Fox
  • P. Francavilla
  • M. Franchini
  • S. Franchino
  • D. Francis
  • M. Franklin
  • S. Franz
  • M. Fraternali
  • S. Fratina
  • S. T. French
  • C. Friedrich
  • F. Friedrich
  • D. Froidevaux
  • J. A. Frost
  • C. Fukunaga
  • E. Fullana Torregrosa
  • B. G. Fulsom
  • J. Fuster
  • C. Gabaldon
  • O. Gabizon
  • A. Gabrielli
  • A. Gabrielli
  • S. Gadatsch
  • T. Gadfort
  • S. Gadomski
  • G. Gagliardi
  • P. Gagnon
  • C. Galea
  • B. Galhardo
  • E. J. Gallas
  • V. Gallo
  • B. J. Gallop
  • P. Gallus
  • G. Galster
  • K. K. Gan
  • R. P. Gandrajula
  • J. Gao
  • Y. S. Gao
  • F. M. Garay Walls
  • F. Garberson
  • C. García
  • J. E. García Navarro
  • M. Garcia-Sciveres
  • R. W. Gardner
  • N. Garelli
  • V. Garonne
  • C. Gatti
  • G. Gaudio
  • B. Gaur
  • L. Gauthier
  • P. Gauzzi
  • I. L. Gavrilenko
  • C. Gay
  • G. Gaycken
  • E. N. Gazis
  • P. Ge
  • Z. Gecse
  • C. N. P. Gee
  • D. A. A. Geerts
  • Ch. Geich-Gimbel
  • K. Gellerstedt
  • C. Gemme
  • A. Gemmell
  • M. H. Genest
  • S. Gentile
  • M. George
  • S. George
  • D. Gerbaudo
  • A. Gershon
  • H. Ghazlane
  • N. Ghodbane
  • B. Giacobbe
  • S. Giagu
  • V. Giangiobbe
  • P. Giannetti
  • F. Gianotti
  • B. Gibbard
  • S. M. Gibson
  • M. Gilchriese
  • T. P. S. Gillam
  • D. Gillberg
  • A. R. Gillman
  • D. M. Gingrich
  • N. Giokaris
  • M. P. Giordani
  • R. Giordano
  • F. M. Giorgi
  • P. Giovannini
  • P. F. Giraud
  • D. Giugni
  • C. Giuliani
  • M. Giunta
  • B. K. Gjelsten
  • I. Gkialas
  • L. K. Gladilin
  • C. Glasman
  • J. Glatzer
  • A. Glazov
  • G. L. Glonti
  • M. Goblirsch-Kolb
  • J. R. Goddard
  • J. Godfrey
  • J. Godlewski
  • C. Goeringer
  • S. Goldfarb
  • T. Golling
  • D. Golubkov
  • A. Gomes
  • L. S. Gomez Fajardo
  • R. Gonçalo
  • J. Goncalves Pinto Firmino Da Costa
  • L. Gonella
  • S. González de la Hoz
  • G. Gonzalez Parra
  • M. L. Gonzalez Silva
  • S. Gonzalez-Sevilla
  • J. J. Goodson
  • L. Goossens
  • P. A. Gorbounov
  • H. A. Gordon
  • I. Gorelov
  • G. Gorfine
  • B. Gorini
  • E. Gorini
  • A. Gorišek
  • E. Gornicki
  • A. T. Goshaw
  • C. Gössling
  • M. I. Gostkin
  • M. Gouighri
  • D. Goujdami
  • M. P. Goulette
  • A. G. Goussiou
  • C. Goy
  • S. Gozpinar
  • H. M. X. Grabas
  • L. Graber
  • I. Grabowska-Bold
  • P. Grafström
  • K-J. Grahn
  • J. Gramling
  • E. Gramstad
  • F. Grancagnolo
  • S. Grancagnolo
  • V. Grassi
  • V. Gratchev
  • H. M. Gray
  • J. A. Gray
  • E. Graziani
  • O. G. Grebenyuk
  • Z. D. Greenwood
  • K. Gregersen
  • I. M. Gregor
  • P. Grenier
  • J. Griffiths
  • N. Grigalashvili
  • A. A. Grillo
  • K. Grimm
  • S. Grinstein
  • Ph. Gris
  • Y. V. Grishkevich
  • J.-F. Grivaz
  • J. P. Grohs
  • A. Grohsjean
  • E. Gross
  • J. Grosse-Knetter
  • G. C. Grossi
  • J. Groth-Jensen
  • Z. J. Grout
  • K. Grybel
  • F. Guescini
  • D. Guest
  • O. Gueta
  • C. Guicheney
  • E. Guido
  • T. Guillemin
  • S. Guindon
  • U. Gul
  • C. Gumpert
  • J. Gunther
  • J. Guo
  • S. Gupta
  • P. Gutierrez
  • N. G. Gutierrez Ortiz
  • C. Gutschow
  • N. Guttman
  • C. Guyot
  • C. Gwenlan
  • C. B. Gwilliam
  • A. Haas
  • C. Haber
  • H. K. Hadavand
  • P. Haefner
  • S. Hageboeck
  • Z. Hajduk
  • H. Hakobyan
  • M. Haleem
  • D. Hall
  • G. Halladjian
  • K. Hamacher
  • P. Hamal
  • K. Hamano
  • M. Hamer
  • A. Hamilton
  • S. Hamilton
  • L. Han
  • K. Hanagaki
  • K. Hanawa
  • M. Hance
  • P. Hanke
  • J. R. Hansen
  • J. B. Hansen
  • J. D. Hansen
  • P. H. Hansen
  • P. Hansson
  • K. Hara
  • A. S. Hard
  • T. Harenberg
  • S. Harkusha
  • D. Harper
  • R. D. Harrington
  • O. M. Harris
  • P. F. Harrison
  • F. Hartjes
  • A. Harvey
  • S. Hasegawa
  • Y. Hasegawa
  • S. Hassani
  • S. Haug
  • M. Hauschild
  • R. Hauser
  • M. Havranek
  • C. M. Hawkes
  • R. J. Hawkings
  • A. D. Hawkins
  • T. Hayashi
  • D. Hayden
  • C. P. Hays
  • H. S. Hayward
  • S. J. Haywood
  • S. J. Head
  • T. Heck
  • V. Hedberg
  • L. Heelan
  • S. Heim
  • B. Heinemann
  • S. Heisterkamp
  • J. Hejbal
  • L. Helary
  • C. Heller
  • M. Heller
  • S. Hellman
  • D. Hellmich
  • C. Helsens
  • J. Henderson
  • R. C. W. Henderson
  • A. Henrichs
  • A. M. Henriques Correia
  • S. Henrot-Versille
  • C. Hensel
  • G. H. Herbert
  • C. M. Hernandez
  • Y. Hernández Jiménez
  • R. Herrberg-Schubert
  • G. Herten
  • R. Hertenberger
  • L. Hervas
  • G. G. Hesketh
  • N. P. Hessey
  • R. Hickling
  • E. Higón-Rodriguez
  • J. C. Hill
  • K. H. Hiller
  • S. Hillert
  • S. J. Hillier
  • I. Hinchliffe
  • E. Hines
  • M. Hirose
  • D. Hirschbuehl
  • J. Hobbs
  • N. Hod
  • M. C. Hodgkinson
  • P. Hodgson
  • A. Hoecker
  • M. R. Hoeferkamp
  • J. Hoffman
  • D. Hoffmann
  • J. I. Hofmann
  • M. Hohlfeld
  • T. R. Holmes
  • T. M. Hong
  • L. Hooft van Huysduynen
  • J-Y. Hostachy
  • S. Hou
  • A. Hoummada
  • J. Howard
  • J. Howarth
  • M. Hrabovsky
  • I. Hristova
  • J. Hrivnac
  • T. Hryn’ova
  • P. J. Hsu
  • S.-C. Hsu
  • D. Hu
  • X. Hu
  • Y. Huang
  • Z. Hubacek
  • F. Hubaut
  • F. Huegging
  • A. Huettmann
  • T. B. Huffman
  • E. W. Hughes
  • G. Hughes
  • M. Huhtinen
  • T. A. Hülsing
  • M. Hurwitz
  • N. Huseynov
  • J. Huston
  • J. Huth
  • G. Iacobucci
  • G. Iakovidis
  • I. Ibragimov
  • L. Iconomidou-Fayard
  • J. Idarraga
  • E. Ideal
  • P. Iengo
  • O. Igonkina
  • T. Iizawa
  • Y. Ikegami
  • K. Ikematsu
  • M. Ikeno
  • D. Iliadis
  • N. Ilic
  • Y. Inamaru
  • T. Ince
  • P. Ioannou
  • M. Iodice
  • K. Iordanidou
  • V. Ippolito
  • A. Irles Quiles
  • C. Isaksson
  • M. Ishino
  • M. Ishitsuka
  • R. Ishmukhametov
  • C. Issever
  • S. Istin
  • A. V. Ivashin
  • W. Iwanski
  • H. Iwasaki
  • J. M. Izen
  • V. Izzo
  • B. Jackson
  • J. N. Jackson
  • M. Jackson
  • P. Jackson
  • M. R. Jaekel
  • V. Jain
  • K. Jakobs
  • S. Jakobsen
  • T. Jakoubek
  • J. Jakubek
  • D. O. Jamin
  • D. K. Jana
  • E. Jansen
  • H. Jansen
  • J. Janssen
  • M. Janus
  • R. C. Jared
  • G. Jarlskog
  • L. Jeanty
  • G.-Y. Jeng
  • I. Jen-La Plante
  • D. Jennens
  • P. Jenni
  • J. Jentzsch
  • C. Jeske
  • S. Jézéquel
  • M. K. Jha
  • H. Ji
  • W. Ji
  • J. Jia
  • Y. Jiang
  • M. Jimenez Belenguer
  • S. Jin
  • A. Jinaru
  • O. Jinnouchi
  • M. D. Joergensen
  • D. Joffe
  • K. E. Johansson
  • P. Johansson
  • K. A. Johns
  • K. Jon-And
  • G. Jones
  • R. W. L. Jones
  • T. J. Jones
  • P. M. Jorge
  • K. D. Joshi
  • J. Jovicevic
  • X. Ju
  • C. A. Jung
  • R. M. Jungst
  • P. Jussel
  • A. Juste Rozas
  • M. Kaci
  • A. Kaczmarska
  • P. Kadlecik
  • M. Kado
  • H. Kagan
  • M. Kagan
  • E. Kajomovitz
  • S. Kalinin
  • S. Kama
  • N. Kanaya
  • M. Kaneda
  • S. Kaneti
  • T. Kanno
  • V. A. Kantserov
  • J. Kanzaki
  • B. Kaplan
  • A. Kapliy
  • D. Kar
  • K. Karakostas
  • N. Karastathis
  • M. Karnevskiy
  • S. N. Karpov
  • K. Karthik
  • V. Kartvelishvili
  • A. N. Karyukhin
  • L. Kashif
  • G. Kasieczka
  • R. D. Kass
  • A. Kastanas
  • Y. Kataoka
  • A. Katre
  • J. Katzy
  • V. Kaushik
  • K. Kawagoe
  • T. Kawamoto
  • G. Kawamura
  • S. Kazama
  • V. F. Kazanin
  • M. Y. Kazarinov
  • R. Keeler
  • P. T. Keener
  • R. Kehoe
  • M. Keil
  • J. S. Keller
  • H. Keoshkerian
  • O. Kepka
  • B. P. Kerševan
  • S. Kersten
  • K. Kessoku
  • J. Keung
  • F. Khalil-zada
  • H. Khandanyan
  • A. Khanov
  • D. Kharchenko
  • A. Khodinov
  • A. Khomich
  • T. J. Khoo
  • G. Khoriauli
  • A. Khoroshilov
  • V. Khovanskiy
  • E. Khramov
  • J. Khubua
  • H. Kim
  • S. H. Kim
  • N. Kimura
  • O. Kind
  • B. T. King
  • M. King
  • R. S. B. King
  • S. B. King
  • J. Kirk
  • A. E. Kiryunin
  • T. Kishimoto
  • D. Kisielewska
  • T. Kitamura
  • T. Kittelmann
  • K. Kiuchi
  • E. Kladiva
  • M. Klein
  • U. Klein
  • K. Kleinknecht
  • P. Klimek
  • A. Klimentov
  • R. Klingenberg
  • J. A. Klinger
  • E. B. Klinkby
  • T. Klioutchnikova
  • P. F. Klok
  • E.-E. Kluge
  • P. Kluit
  • S. Kluth
  • E. Kneringer
  • E. B. F. G. Knoops
  • A. Knue
  • T. Kobayashi
  • M. Kobel
  • M. Kocian
  • P. Kodys
  • S. Koenig
  • P. Koevesarki
  • T. Koffas
  • E. Koffeman
  • L. A. Kogan
  • S. Kohlmann
  • Z. Kohout
  • T. Kohriki
  • T. Koi
  • H. Kolanoski
  • I. Koletsou
  • J. Koll
  • A. A. Komar
  • Y. Komori
  • T. Kondo
  • K. Köneke
  • A. C. König
  • T. Kono
  • R. Konoplich
  • N. Konstantinidis
  • R. Kopeliansky
  • S. Koperny
  • L. Köpke
  • A. K. Kopp
  • K. Korcyl
  • K. Kordas
  • A. Korn
  • A. A. Korol
  • I. Korolkov
  • E. V. Korolkova
  • V. A. Korotkov
  • O. Kortner
  • S. Kortner
  • V. V. Kostyukhin
  • S. Kotov
  • V. M. Kotov
  • A. Kotwal
  • C. Kourkoumelis
  • V. Kouskoura
  • A. Koutsman
  • R. Kowalewski
  • T. Z. Kowalski
  • W. Kozanecki
  • A. S. Kozhin
  • V. Kral
  • V. A. Kramarenko
  • G. Kramberger
  • M. W. Krasny
  • A. Krasznahorkay
  • J. K. Kraus
  • A. Kravchenko
  • S. Kreiss
  • J. Kretzschmar
  • K. Kreutzfeldt
  • N. Krieger
  • P. Krieger
  • K. Kroeninger
  • H. Kroha
  • J. Kroll
  • J. Kroseberg
  • J. Krstic
  • U. Kruchonak
  • H. Krüger
  • T. Kruker
  • N. Krumnack
  • Z. V. Krumshteyn
  • A. Kruse
  • M. C. Kruse
  • M. Kruskal
  • T. Kubota
  • S. Kuday
  • S. Kuehn
  • A. Kugel
  • T. Kuhl
  • V. Kukhtin
  • Y. Kulchitsky
  • S. Kuleshov
  • M. Kuna
  • J. Kunkle
  • A. Kupco
  • H. Kurashige
  • M. Kurata
  • Y. A. Kurochkin
  • R. Kurumida
  • V. Kus
  • E. S. Kuwertz
  • M. Kuze
  • J. Kvita
  • R. Kwee
  • A. La Rosa
  • L. La Rotonda
  • L. Labarga
  • S. Lablak
  • C. Lacasta
  • F. Lacava
  • J. Lacey
  • H. Lacker
  • D. Lacour
  • V. R. Lacuesta
  • E. Ladygin
  • R. Lafaye
  • B. Laforge
  • T. Lagouri
  • S. Lai
  • H. Laier
  • E. Laisne
  • L. Lambourne
  • C. L. Lampen
  • W. Lampl
  • E. Lançon
  • U. Landgraf
  • M. P. J. Landon
  • V. S. Lang
  • C. Lange
  • A. J. Lankford
  • F. Lanni
  • K. Lantzsch
  • A. Lanza
  • S. Laplace
  • C. Lapoire
  • J. F. Laporte
  • T. Lari
  • A. Larner
  • M. Lassnig
  • P. Laurelli
  • V. Lavorini
  • W. Lavrijsen
  • P. Laycock
  • B. T. Le
  • O. Le Dortz
  • E. Le Guirriec
  • E. Le Menedeu
  • T. LeCompte
  • F. Ledroit-Guillon
  • C. A. Lee
  • H. Lee
  • J. S. H. Lee
  • S. C. Lee
  • L. Lee
  • G. Lefebvre
  • M. Lefebvre
  • F. Legger
  • C. Leggett
  • A. Lehan
  • M. Lehmacher
  • G. Lehmann Miotto
  • A. G. Leister
  • M. A. L. Leite
  • R. Leitner
  • D. Lellouch
  • B. Lemmer
  • V. Lendermann
  • K. J. C. Leney
  • T. Lenz
  • G. Lenzen
  • B. Lenzi
  • R. Leone
  • K. Leonhardt
  • S. Leontsinis
  • C. Leroy
  • J-R. Lessard
  • C. G. Lester
  • C. M. Lester
  • J. Levêque
  • D. Levin
  • L. J. Levinson
  • A. Lewis
  • G. H. Lewis
  • A. M. Leyko
  • M. Leyton
  • B. Li
  • B. Li
  • H. Li
  • H. L. Li
  • S. Li
  • X. Li
  • Z. Liang
  • H. Liao
  • B. Liberti
  • P. Lichard
  • K. Lie
  • J. Liebal
  • W. Liebig
  • C. Limbach
  • A. Limosani
  • M. Limper
  • S. C. Lin
  • F. Linde
  • B. E. Lindquist
  • J. T. Linnemann
  • E. Lipeles
  • A. Lipniacka
  • M. Lisovyi
  • T. M. Liss
  • D. Lissauer
  • A. Lister
  • A. M. Litke
  • B. Liu
  • D. Liu
  • J. B. Liu
  • K. Liu
  • L. Liu
  • M. Liu
  • M. Liu
  • Y. Liu
  • M. Livan
  • S. S. A. Livermore
  • A. Lleres
  • J. Llorente Merino
  • S. L. Lloyd
  • F. Lo Sterzo
  • E. Lobodzinska
  • P. Loch
  • W. S. Lockman
  • T. Loddenkoetter
  • F. K. Loebinger
  • A. E. Loevschall-Jensen
  • A. Loginov
  • C. W. Loh
  • T. Lohse
  • K. Lohwasser
  • M. Lokajicek
  • V. P. Lombardo
  • J. D. Long
  • R. E. Long
  • L. Lopes
  • D. Lopez Mateos
  • B. Lopez Paredes
  • J. Lorenz
  • N. Lorenzo Martinez
  • M. Losada
  • P. Loscutoff
  • M. J. Losty
  • X. Lou
  • A. Lounis
  • J. Love
  • P. A. Love
  • A. J. Lowe
  • F. Lu
  • H. J. Lubatti
  • C. Luci
  • A. Lucotte
  • D. Ludwig
  • I. Ludwig
  • F. Luehring
  • W. Lukas
  • L. Luminari
  • E. Lund
  • J. Lundberg
  • O. Lundberg
  • B. Lund-Jensen
  • M. Lungwitz
  • D. Lynn
  • R. Lysak
  • E. Lytken
  • H. Ma
  • L. L. Ma
  • G. Maccarrone
  • A. Macchiolo
  • B. Maček
  • J. Machado Miguens
  • D. Macina
  • R. Mackeprang
  • R. Madar
  • R. J. Madaras
  • H. J. Maddocks
  • W. F. Mader
  • A. Madsen
  • M. Maeno
  • T. Maeno
  • L. Magnoni
  • E. Magradze
  • K. Mahboubi
  • J. Mahlstedt
  • S. Mahmoud
  • G. Mahout
  • C. Maiani
  • C. Maidantchik
  • A. Maio
  • S. Majewski
  • Y. Makida
  • N. Makovec
  • P. Mal
  • B. Malaescu
  • Pa. Malecki
  • V. P. Maleev
  • F. Malek
  • U. Mallik
  • D. Malon
  • C. Malone
  • S. Maltezos
  • V. M. Malyshev
  • S. Malyukov
  • J. Mamuzic
  • L. Mandelli
  • I. Mandić
  • R. Mandrysch
  • J. Maneira
  • A. Manfredini
  • L. Manhaes de Andrade Filho
  • J. A. Manjarres Ramos
  • A. Mann
  • P. M. Manning
  • A. Manousakis-Katsikakis
  • B. Mansoulie
  • R. Mantifel
  • L. Mapelli
  • L. March
  • J. F. Marchand
  • F. Marchese
  • G. Marchiori
  • M. Marcisovsky
  • C. P. Marino
  • C. N. Marques
  • F. Marroquim
  • Z. Marshall
  • L. F. Marti
  • S. Marti-Garcia
  • B. Martin
  • B. Martin
  • J. P. Martin
  • T. A. Martin
  • V. J. Martin
  • B. Martin dit Latour
  • H. Martinez
  • M. Martinez
  • S. Martin-Haugh
  • A. C. Martyniuk
  • M. Marx
  • F. Marzano
  • A. Marzin
  • L. Masetti
  • T. Mashimo
  • R. Mashinistov
  • J. Masik
  • A. L. Maslennikov
  • I. Massa
  • N. Massol
  • P. Mastrandrea
  • A. Mastroberardino
  • T. Masubuchi
  • H. Matsunaga
  • T. Matsushita
  • P. Mättig
  • S. Mättig
  • J. Mattmann
  • C. Mattravers
  • J. Maurer
  • S. J. Maxfield
  • D. A. Maximov
  • R. Mazini
  • L. Mazzaferro
  • M. Mazzanti
  • G. Mc Goldrick
  • S. P. Mc Kee
  • A. McCarn
  • R. L. McCarthy
  • T. G. McCarthy
  • N. A. McCubbin
  • K. W. McFarlane
  • J. A. Mcfayden
  • G. Mchedlidze
  • T. Mclaughlan
  • S. J. McMahon
  • R. A. McPherson
  • A. Meade
  • J. Mechnich
  • M. Mechtel
  • M. Medinnis
  • S. Meehan
  • R. Meera-Lebbai
  • S. Mehlhase
  • A. Mehta
  • K. Meier
  • C. Meineck
  • B. Meirose
  • C. Melachrinos
  • B. R. Mellado Garcia
  • F. Meloni
  • L. Mendoza Navas
  • A. Mengarelli
  • S. Menke
  • E. Meoni
  • K. M. Mercurio
  • S. Mergelmeyer
  • N. Meric
  • P. Mermod
  • L. Merola
  • C. Meroni
  • F. S. Merritt
  • H. Merritt
  • A. Messina
  • J. Metcalfe
  • A. S. Mete
  • C. Meyer
  • C. Meyer
  • J-P. Meyer
  • J. Meyer
  • J. Meyer
  • S. Michal
  • R. P. Middleton
  • S. Migas
  • L. Mijović
  • G. Mikenberg
  • M. Mikestikova
  • M. Mikuž
  • D. W. Miller
  • C. Mills
  • A. Milov
  • D. A. Milstead
  • D. Milstein
  • A. A. Minaenko
  • M. Miñano Moya
  • I. A. Minashvili
  • A. I. Mincer
  • B. Mindur
  • M. Mineev
  • Y. Ming
  • L. M. Mir
  • G. Mirabelli
  • T. Mitani
  • J. Mitrevski
  • V. A. Mitsou
  • S. Mitsui
  • P. S. Miyagawa
  • J. U. Mjörnmark
  • T. Moa
  • V. Moeller
  • S. Mohapatra
  • W. Mohr
  • S. Molander
  • R. Moles-Valls
  • A. Molfetas
  • K. Mönig
  • C. Monini
  • J. Monk
  • E. Monnier
  • J. Montejo Berlingen
  • F. Monticelli
  • S. Monzani
  • R. W. Moore
  • C. Mora Herrera
  • A. Moraes
  • N. Morange
  • J. Morel
  • D. Moreno
  • M. Moreno Llácer
  • P. Morettini
  • M. Morgenstern
  • M. Morii
  • S. Moritz
  • A. K. Morley
  • G. Mornacchi
  • J. D. Morris
  • L. Morvaj
  • H. G. Moser
  • M. Mosidze
  • J. Moss
  • R. Mount
  • E. Mountricha
  • S. V. Mouraviev
  • E. J. W. Moyse
  • R. D. Mudd
  • F. Mueller
  • J. Mueller
  • K. Mueller
  • T. Mueller
  • T. Mueller
  • D. Muenstermann
  • Y. Munwes
  • J. A. Murillo Quijada
  • W. J. Murray
  • I. Mussche
  • E. Musto
  • A. G. Myagkov
  • M. Myska
  • O. Nackenhorst
  • J. Nadal
  • K. Nagai
  • R. Nagai
  • Y. Nagai
  • K. Nagano
  • A. Nagarkar
  • Y. Nagasaka
  • M. Nagel
  • A. M. Nairz
  • Y. Nakahama
  • K. Nakamura
  • T. Nakamura
  • I. Nakano
  • H. Namasivayam
  • G. Nanava
  • A. Napier
  • R. Narayan
  • M. Nash
  • T. Nattermann
  • T. Naumann
  • G. Navarro
  • H. A. Neal
  • P. Yu. Nechaeva
  • T. J. Neep
  • A. Negri
  • G. Negri
  • M. Negrini
  • S. Nektarijevic
  • A. Nelson
  • T. K. Nelson
  • S. Nemecek
  • P. Nemethy
  • A. A. Nepomuceno
  • M. Nessi
  • M. S. Neubauer
  • M. Neumann
  • A. Neusiedl
  • R. M. Neves
  • P. Nevski
  • F. M. Newcomer
  • P. R. Newman
  • D. H. Nguyen
  • V. Nguyen Thi Hong
  • R. B. Nickerson
  • R. Nicolaidou
  • B. Nicquevert
  • J. Nielsen
  • N. Nikiforou
  • A. Nikiforov
  • V. Nikolaenko
  • I. Nikolic-Audit
  • K. Nikolics
  • K. Nikolopoulos
  • P. Nilsson
  • Y. Ninomiya
  • A. Nisati
  • R. Nisius
  • T. Nobe
  • L. Nodulman
  • M. Nomachi
  • I. Nomidis
  • S. Norberg
  • M. Nordberg
  • J. Novakova
  • M. Nozaki
  • L. Nozka
  • K. Ntekas
  • A.-E. Nuncio-Quiroz
  • G. Nunes Hanninger
  • T. Nunnemann
  • E. Nurse
  • B. J. O’Brien
  • F. O’Grady
  • D. C. O’Neil
  • V. O’Shea
  • L. B. Oakes
  • F. G. Oakham
  • H. Oberlack
  • J. Ocariz
  • A. Ochi
  • M. I. Ochoa
  • S. Oda
  • S. Odaka
  • H. Ogren
  • A. Oh
  • S. H. Oh
  • C. C. Ohm
  • T. Ohshima
  • W. Okamura
  • H. Okawa
  • Y. Okumura
  • T. Okuyama
  • A. Olariu
  • A. G. Olchevski
  • S. A. Olivares Pino
  • M. Oliveira
  • D. Oliveira Damazio
  • E. Oliver Garcia
  • D. Olivito
  • A. Olszewski
  • J. Olszowska
  • A. Onofre
  • P. U. E. Onyisi
  • C. J. Oram
  • M. J. Oreglia
  • Y. Oren
  • D. Orestano
  • N. Orlando
  • C. Oropeza Barrera
  • R. S. Orr
  • B. Osculati
  • R. Ospanov
  • G. Otero y Garzon
  • H. Otono
  • M. Ouchrif
  • E. A. Ouellette
  • F. Ould-Saada
  • A. Ouraou
  • K. P. Oussoren
  • Q. Ouyang
  • A. Ovcharova
  • M. Owen
  • S. Owen
  • V. E. Ozcan
  • N. Ozturk
  • K. Pachal
  • A. Pacheco Pages
  • C. Padilla Aranda
  • S. Pagan Griso
  • E. Paganis
  • C. Pahl
  • F. Paige
  • P. Pais
  • K. Pajchel
  • G. Palacino
  • S. Palestini
  • D. Pallin
  • A. Palma
  • J. D. Palmer
  • Y. B. Pan
  • E. Panagiotopoulou
  • J. G. Panduro Vazquez
  • P. Pani
  • N. Panikashvili
  • S. Panitkin
  • D. Pantea
  • Th. D. Papadopoulou
  • K. Papageorgiou
  • A. Paramonov
  • D. Paredes Hernandez
  • M. A. Parker
  • F. Parodi
  • J. A. Parsons
  • U. Parzefall
  • S. Pashapour
  • E. Pasqualucci
  • S. Passaggio
  • A. Passeri
  • F. Pastore
  • Fr. Pastore
  • G. Pásztor
  • S. Pataraia
  • N. D. Patel
  • J. R. Pater
  • S. Patricelli
  • T. Pauly
  • J. Pearce
  • M. Pedersen
  • S. Pedraza Lopez
  • R. Pedro
  • S. V. Peleganchuk
  • D. Pelikan
  • H. Peng
  • B. Penning
  • J. Penwell
  • D. V. Perepelitsa
  • T. Perez Cavalcanti
  • E. Perez Codina
  • M. T. Pérez García-Estañ
  • V. Perez Reale
  • L. Perini
  • H. Pernegger
  • R. Perrino
  • R. Peschke
  • V. D. Peshekhonov
  • K. Peters
  • R. F. Y. Peters
  • B. A. Petersen
  • J. Petersen
  • T. C. Petersen
  • E. Petit
  • A. Petridis
  • C. Petridou
  • E. Petrolo
  • F. Petrucci
  • M. Petteni
  • R. Pezoa
  • P. W. Phillips
  • G. Piacquadio
  • E. Pianori
  • A. Picazio
  • E. Piccaro
  • M. Piccinini
  • S. M. Piec
  • R. Piegaia
  • D. T. Pignotti
  • J. E. Pilcher
  • A. D. Pilkington
  • J. Pina
  • M. Pinamonti
  • A. Pinder
  • J. L. Pinfold
  • A. Pingel
  • B. Pinto
  • C. Pizio
  • M.-A. Pleier
  • V. Pleskot
  • E. Plotnikova
  • P. Plucinski
  • S. Poddar
  • F. Podlyski
  • R. Poettgen
  • L. Poggioli
  • D. Pohl
  • M. Pohl
  • G. Polesello
  • A. Policicchio
  • R. Polifka
  • A. Polini
  • C. S. Pollard
  • V. Polychronakos
  • D. Pomeroy
  • K. Pommès
  • L. Pontecorvo
  • B. G. Pope
  • G. A. Popeneciu
  • D. S. Popovic
  • A. Poppleton
  • X. Portell Bueso
  • G. E. Pospelov
  • S. Pospisil
  • K. Potamianos
  • I. N. Potrap
  • C. J. Potter
  • C. T. Potter
  • G. Poulard
  • J. Poveda
  • V. Pozdnyakov
  • R. Prabhu
  • P. Pralavorio
  • A. Pranko
  • S. Prasad
  • R. Pravahan
  • S. Prell
  • D. Price
  • J. Price
  • L. E. Price
  • D. Prieur
  • M. Primavera
  • M. Proissl
  • K. Prokofiev
  • F. Prokoshin
  • E. Protopapadaki
  • S. Protopopescu
  • J. Proudfoot
  • X. Prudent
  • M. Przybycien
  • H. Przysiezniak
  • S. Psoroulas
  • E. Ptacek
  • E. Pueschel
  • D. Puldon
  • M. Purohit
  • P. Puzo
  • Y. Pylypchenko
  • J. Qian
  • A. Quadt
  • D. R. Quarrie
  • W. B. Quayle
  • D. Quilty
  • V. Radeka
  • V. Radescu
  • S. K. Radhakrishnan
  • P. Radloff
  • F. Ragusa
  • G. Rahal
  • S. Rajagopalan
  • M. Rammensee
  • M. Rammes
  • A. S. Randle-Conde
  • C. Rangel-Smith
  • K. Rao
  • F. Rauscher
  • T. C. Rave
  • T. Ravenscroft
  • M. Raymond
  • A. L. Read
  • D. M. Rebuzzi
  • A. Redelbach
  • G. Redlinger
  • R. Reece
  • K. Reeves
  • A. Reinsch
  • H. Reisin
  • I. Reisinger
  • M. Relich
  • C. Rembser
  • Z. L. Ren
  • A. Renaud
  • M. Rescigno
  • S. Resconi
  • B. Resende
  • P. Reznicek
  • R. Rezvani
  • R. Richter
  • M. Ridel
  • P. Rieck
  • M. Rijssenbeek
  • A. Rimoldi
  • L. Rinaldi
  • E. Ritsch
  • I. Riu
  • G. Rivoltella
  • F. Rizatdinova
  • E. Rizvi
  • S. H. Robertson
  • A. Robichaud-Veronneau
  • D. Robinson
  • J. E. M. Robinson
  • A. Robson
  • J. G. Rocha de Lima
  • C. Roda
  • D. Roda Dos Santos
  • L. Rodrigues
  • S. Roe
  • O. Røhne
  • S. Rolli
  • A. Romaniouk
  • M. Romano
  • G. Romeo
  • E. Romero Adam
  • N. Rompotis
  • L. Roos
  • E. Ros
  • S. Rosati
  • K. Rosbach
  • A. Rose
  • M. Rose
  • P. L. Rosendahl
  • O. Rosenthal
  • V. Rossetti
  • E. Rossi
  • L. P. Rossi
  • R. Rosten
  • M. Rotaru
  • I. Roth
  • J. Rothberg
  • D. Rousseau
  • C. R. Royon
  • A. Rozanov
  • Y. Rozen
  • X. Ruan
  • F. Rubbo
  • I. Rubinskiy
  • V. I. Rud
  • C. Rudolph
  • M. S. Rudolph
  • F. Rühr
  • A. Ruiz-Martinez
  • L. Rumyantsev
  • Z. Rurikova
  • N. A. Rusakovich
  • A. Ruschke
  • J. P. Rutherfoord
  • N. Ruthmann
  • P. Ruzicka
  • Y. F. Ryabov
  • M. Rybar
  • G. Rybkin
  • N. C. Ryder
  • A. F. Saavedra
  • S. Sacerdoti
  • A. Saddique
  • I. Sadeh
  • H. F-W. Sadrozinski
  • R. Sadykov
  • F. Safai Tehrani
  • H. Sakamoto
  • Y. Sakurai
  • G. Salamanna
  • A. Salamon
  • M. Saleem
  • D. Salek
  • P. H. Sales De Bruin
  • D. Salihagic
  • A. Salnikov
  • J. Salt
  • B. M. Salvachua Ferrando
  • D. Salvatore
  • F. Salvatore
  • A. Salvucci
  • A. Salzburger
  • D. Sampsonidis
  • A. Sanchez
  • J. Sánchez
  • V. Sanchez Martinez
  • H. Sandaker
  • H. G. Sander
  • M. P. Sanders
  • M. Sandhoff
  • T. Sandoval
  • C. Sandoval
  • R. Sandstroem
  • D. P. C. Sankey
  • A. Sansoni
  • C. Santoni
  • R. Santonico
  • H. Santos
  • I. Santoyo Castillo
  • K. Sapp
  • A. Sapronov
  • J. G. Saraiva
  • E. Sarkisyan-Grinbaum
  • B. Sarrazin
  • G. Sartisohn
  • O. Sasaki
  • Y. Sasaki
  • N. Sasao
  • I. Satsounkevitch
  • G. Sauvage
  • E. Sauvan
  • J. B. Sauvan
  • P. Savard
  • V. Savinov
  • D. O. Savu
  • C. Sawyer
  • L. Sawyer
  • D. H. Saxon
  • J. Saxon
  • C. Sbarra
  • A. Sbrizzi
  • T. Scanlon
  • D. A. Scannicchio
  • M. Scarcella
  • J. Schaarschmidt
  • P. Schacht
  • D. Schaefer
  • A. Schaelicke
  • S. Schaepe
  • S. Schaetzel
  • U. Schäfer
  • A. C. Schaffer
  • D. Schaile
  • R. D. Schamberger
  • V. Scharf
  • V. A. Schegelsky
  • D. Scheirich
  • M. Schernau
  • M. I. Scherzer
  • C. Schiavi
  • J. Schieck
  • C. Schillo
  • M. Schioppa
  • S. Schlenker
  • E. Schmidt
  • K. Schmieden
  • C. Schmitt
  • C. Schmitt
  • S. Schmitt
  • B. Schneider
  • Y. J. Schnellbach
  • U. Schnoor
  • L. Schoeffel
  • A. Schoening
  • B. D. Schoenrock
  • A. L. S. Schorlemmer
  • M. Schott
  • D. Schouten
  • J. Schovancova
  • M. Schram
  • S. Schramm
  • M. Schreyer
  • C. Schroeder
  • N. Schroer
  • N. Schuh
  • M. J. Schultens
  • H.-C. Schultz-Coulon
  • H. Schulz
  • M. Schumacher
  • B. A. Schumm
  • Ph. Schune
  • A. Schwartzman
  • Ph. Schwegler
  • Ph. Schwemling
  • R. Schwienhorst
  • J. Schwindling
  • T. Schwindt
  • M. Schwoerer
  • F. G. Sciacca
  • E. Scifo
  • G. Sciolla
  • W. G. Scott
  • F. Scutti
  • J. Searcy
  • G. Sedov
  • E. Sedykh
  • S. C. Seidel
  • A. Seiden
  • F. Seifert
  • J. M. Seixas
  • G. Sekhniaidze
  • S. J. Sekula
  • K. E. Selbach
  • D. M. Seliverstov
  • G. Sellers
  • M. Seman
  • N. Semprini-Cesari
  • C. Serfon
  • L. Serin
  • L. Serkin
  • T. Serre
  • R. Seuster
  • H. Severini
  • F. Sforza
  • A. Sfyrla
  • E. Shabalina
  • M. Shamim
  • L. Y. Shan
  • J. T. Shank
  • Q. T. Shao
  • M. Shapiro
  • P. B. Shatalov
  • K. Shaw
  • P. Sherwood
  • S. Shimizu
  • M. Shimojima
  • T. Shin
  • M. Shiyakova
  • A. Shmeleva
  • M. J. Shochet
  • D. Short
  • S. Shrestha
  • E. Shulga
  • M. A. Shupe
  • S. Shushkevich
  • P. Sicho
  • D. Sidorov
  • A. Sidoti
  • F. Siegert
  • Dj. Sijacki
  • O. Silbert
  • J. Silva
  • Y. Silver
  • D. Silverstein
  • S. B. Silverstein
  • V. Simak
  • O. Simard
  • Lj. Simic
  • S. Simion
  • E. Simioni
  • B. Simmons
  • R. Simoniello
  • M. Simonyan
  • P. Sinervo
  • N. B. Sinev
  • V. Sipica
  • G. Siragusa
  • A. Sircar
  • A. N. Sisakyan
  • S. Yu. Sivoklokov
  • J. Sjölin
  • T. B. Sjursen
  • L. A. Skinnari
  • H. P. Skottowe
  • K. Yu. Skovpen
  • P. Skubic
  • M. Slater
  • T. Slavicek
  • K. Sliwa
  • V. Smakhtin
  • B. H. Smart
  • L. Smestad
  • S. Yu. Smirnov
  • Y. Smirnov
  • L. N. Smirnova
  • O. Smirnova
  • K. M. Smith
  • M. Smizanska
  • K. Smolek
  • A. A. Snesarev
  • G. Snidero
  • J. Snow
  • S. Snyder
  • R. Sobie
  • F. Socher
  • J. Sodomka
  • A. Soffer
  • D. A. Soh
  • C. A. Solans
  • M. Solar
  • J. Solc
  • E. Yu. Soldatov
  • U. Soldevila
  • E. Solfaroli Camillocci
  • A. A. Solodkov
  • O. V. Solovyanov
  • V. Solovyev
  • N. Soni
  • A. Sood
  • V. Sopko
  • B. Sopko
  • M. Sosebee
  • R. Soualah
  • P. Soueid
  • A. M. Soukharev
  • D. South
  • S. Spagnolo
  • F. Spanò
  • W. R. Spearman
  • R. Spighi
  • G. Spigo
  • M. Spousta
  • T. Spreitzer
  • B. Spurlock
  • R. D. St. Denis
  • J. Stahlman
  • R. Stamen
  • E. Stanecka
  • R. W. Stanek
  • C. Stanescu
  • M. Stanescu-Bellu
  • M. M. Stanitzki
  • S. Stapnes
  • E. A. Starchenko
  • J. Stark
  • P. Staroba
  • P. Starovoitov
  • R. Staszewski
  • P. Stavina
  • G. Steele
  • P. Steinbach
  • P. Steinberg
  • I. Stekl
  • B. Stelzer
  • H. J. Stelzer
  • O. Stelzer-Chilton
  • H. Stenzel
  • S. Stern
  • G. A. Stewart
  • J. A. Stillings
  • M. C. Stockton
  • M. Stoebe
  • K. Stoerig
  • G. Stoicea
  • S. Stonjek
  • A. R. Stradling
  • A. Straessner
  • J. Strandberg
  • S. Strandberg
  • A. Strandlie
  • E. Strauss
  • M. Strauss
  • P. Strizenec
  • R. Ströhmer
  • D. M. Strom
  • R. Stroynowski
  • S. A. Stucci
  • B. Stugu
  • I. Stumer
  • J. Stupak
  • P. Sturm
  • N. A. Styles
  • D. Su
  • J. Su
  • HS. Subramania
  • R. Subramaniam
  • A. Succurro
  • Y. Sugaya
  • C. Suhr
  • M. Suk
  • V. V. Sulin
  • S. Sultansoy
  • T. Sumida
  • X. Sun
  • J. E. Sundermann
  • K. Suruliz
  • G. Susinno
  • M. R. Sutton
  • Y. Suzuki
  • M. Svatos
  • S. Swedish
  • M. Swiatlowski
  • I. Sykora
  • T. Sykora
  • D. Ta
  • K. Tackmann
  • J. Taenzer
  • A. Taffard
  • R. Tafirout
  • N. Taiblum
  • Y. Takahashi
  • H. Takai
  • R. Takashima
  • H. Takeda
  • T. Takeshita
  • Y. Takubo
  • M. Talby
  • A. A. Talyshev
  • J. Y. C. Tam
  • M. C. Tamsett
  • K. G. Tan
  • J. Tanaka
  • R. Tanaka
  • S. Tanaka
  • S. Tanaka
  • A. J. Tanasijczuk
  • K. Tani
  • N. Tannoury
  • S. Tapprogge
  • S. Tarem
  • F. Tarrade
  • G. F. Tartarelli
  • P. Tas
  • M. Tasevsky
  • T. Tashiro
  • E. Tassi
  • A. Tavares Delgado
  • Y. Tayalati
  • C. Taylor
  • F. E. Taylor
  • G. N. Taylor
  • W. Taylor
  • F. A. Teischinger
  • M. Teixeira Dias Castanheira
  • P. Teixeira-Dias
  • K. K. Temming
  • H. Ten Kate
  • P. K. Teng
  • S. Terada
  • K. Terashi
  • J. Terron
  • S. Terzo
  • M. Testa
  • R. J. Teuscher
  • J. Therhaag
  • T. Theveneaux-Pelzer
  • S. Thoma
  • J. P. Thomas
  • E. N. Thompson
  • P. D. Thompson
  • P. D. Thompson
  • A. S. Thompson
  • L. A. Thomsen
  • E. Thomson
  • M. Thomson
  • W. M. Thong
  • R. P. Thun
  • F. Tian
  • M. J. Tibbetts
  • T. Tic
  • V. O. Tikhomirov
  • Yu. A. Tikhonov
  • S. Timoshenko
  • E. Tiouchichine
  • P. Tipton
  • S. Tisserant
  • T. Todorov
  • S. Todorova-Nova
  • B. Toggerson
  • J. Tojo
  • S. Tokár
  • K. Tokushuku
  • K. Tollefson
  • L. Tomlinson
  • M. Tomoto
  • L. Tompkins
  • K. Toms
  • N. D. Topilin
  • E. Torrence
  • H. Torres
  • E. Torró Pastor
  • J. Toth
  • F. Touchard
  • D. R. Tovey
  • H. L. Tran
  • T. Trefzger
  • L. Tremblet
  • A. Tricoli
  • I. M. Trigger
  • S. Trincaz-Duvoid
  • M. F. Tripiana
  • N. Triplett
  • W. Trischuk
  • B. Trocmé
  • C. Troncon
  • M. Trottier-McDonald
  • M. Trovatelli
  • P. True
  • M. Trzebinski
  • A. Trzupek
  • C. Tsarouchas
  • J. C-L. Tseng
  • P. V. Tsiareshka
  • D. Tsionou
  • G. Tsipolitis
  • N. Tsirintanis
  • S. Tsiskaridze
  • V. Tsiskaridze
  • E. G. Tskhadadze
  • I. I. Tsukerman
  • V. Tsulaia
  • J.-W. Tsung
  • S. Tsuno
  • D. Tsybychev
  • A. Tua
  • A. Tudorache
  • V. Tudorache
  • J. M. Tuggle
  • A. N. Tuna
  • S. A. Tupputi
  • S. Turchikhin
  • D. Turecek
  • I. Turk Cakir
  • R. Turra
  • P. M. Tuts
  • A. Tykhonov
  • M. Tylmad
  • M. Tyndel
  • K. Uchida
  • I. Ueda
  • R. Ueno
  • M. Ughetto
  • M. Ugland
  • M. Uhlenbrock
  • F. Ukegawa
  • G. Unal
  • A. Undrus
  • G. Unel
  • F. C. Ungaro
  • Y. Unno
  • D. Urbaniec
  • P. Urquijo
  • G. Usai
  • A. Usanova
  • L. Vacavant
  • V. Vacek
  • B. Vachon
  • N. Valencic
  • S. Valentinetti
  • A. Valero
  • L. Valery
  • S. Valkar
  • E. Valladolid Gallego
  • S. Vallecorsa
  • J. A. Valls Ferrer
  • R. Van Berg
  • P. C. Van Der Deijl
  • R. van der Geer
  • H. van der Graaf
  • R. Van Der Leeuw
  • D. van der Ster
  • N. van Eldik
  • P. van Gemmeren
  • J. Van Nieuwkoop
  • I. van Vulpen
  • M. C. van Woerden
  • M. Vanadia
  • W. Vandelli
  • A. Vaniachine
  • P. Vankov
  • F. Vannucci
  • G. Vardanyan
  • R. Vari
  • E. W. Varnes
  • T. Varol
  • D. Varouchas
  • A. Vartapetian
  • K. E. Varvell
  • V. I. Vassilakopoulos
  • F. Vazeille
  • T. Vazquez Schroeder
  • J. Veatch
  • F. Veloso
  • S. Veneziano
  • A. Ventura
  • D. Ventura
  • M. Venturi
  • N. Venturi
  • A. Venturini
  • V. Vercesi
  • M. Verducci
  • W. Verkerke
  • J. C. Vermeulen
  • A. Vest
  • M. C. Vetterli
  • O. Viazlo
  • I. Vichou
  • T. Vickey
  • O. E. Vickey Boeriu
  • G. H. A. Viehhauser
  • S. Viel
  • R. Vigne
  • M. Villa
  • M. Villaplana Perez
  • E. Vilucchi
  • M. G. Vincter
  • V. B. Vinogradov
  • J. Virzi
  • O. Vitells
  • M. Viti
  • I. Vivarelli
  • F. Vives Vaque
  • S. Vlachos
  • D. Vladoiu
  • M. Vlasak
  • A. Vogel
  • P. Vokac
  • G. Volpi
  • M. Volpi
  • G. Volpini
  • H. von der Schmitt
  • H. von Radziewski
  • E. von Toerne
  • V. Vorobel
  • M. Vos
  • R. Voss
  • J. H. Vossebeld
  • N. Vranjes
  • M. Vranjes Milosavljevic
  • V. Vrba
  • M. Vreeswijk
  • T. Vu Anh
  • R. Vuillermet
  • I. Vukotic
  • Z. Vykydal
  • W. Wagner
  • P. Wagner
  • S. Wahrmund
  • J. Wakabayashi
  • S. Walch
  • J. Walder
  • R. Walker
  • W. Walkowiak
  • R. Wall
  • P. Waller
  • B. Walsh
  • C. Wang
  • H. Wang
  • H. Wang
  • J. Wang
  • J. Wang
  • K. Wang
  • R. Wang
  • S. M. Wang
  • T. Wang
  • X. Wang
  • A. Warburton
  • C. P. Ward
  • D. R. Wardrope
  • M. Warsinsky
  • A. Washbrook
  • C. Wasicki
  • I. Watanabe
  • P. M. Watkins
  • A. T. Watson
  • I. J. Watson
  • M. F. Watson
  • G. Watts
  • S. Watts
  • A. T. Waugh
  • B. M. Waugh
  • S. Webb
  • M. S. Weber
  • S. W. Weber
  • J. S. Webster
  • A. R. Weidberg
  • P. Weigell
  • J. Weingarten
  • C. Weiser
  • H. Weits
  • P. S. Wells
  • T. Wenaus
  • D. Wendland
  • Z. Weng
  • T. Wengler
  • S. Wenig
  • N. Wermes
  • M. Werner
  • P. Werner
  • M. Wessels
  • J. Wetter
  • K. Whalen
  • A. White
  • M. J. White
  • R. White
  • S. White
  • D. Whiteson
  • D. Whittington
  • D. Wicke
  • F. J. Wickens
  • W. Wiedenmann
  • M. Wielers
  • P. Wienemann
  • C. Wiglesworth
  • L. A. M. Wiik-Fuchs
  • P. A. Wijeratne
  • A. Wildauer
  • M. A. Wildt
  • I. Wilhelm
  • H. G. Wilkens
  • J. Z. Will
  • H. H. Williams
  • S. Williams
  • W. Willis
  • S. Willocq
  • J. A. Wilson
  • A. Wilson
  • I. Wingerter-Seez
  • S. Winkelmann
  • F. Winklmeier
  • M. Wittgen
  • T. Wittig
  • J. Wittkowski
  • S. J. Wollstadt
  • M. W. Wolter
  • H. Wolters
  • W. C. Wong
  • B. K. Wosiek
  • J. Wotschack
  • M. J. Woudstra
  • K. W. Wozniak
  • K. Wraight
  • M. Wright
  • S. L. Wu
  • X. Wu
  • Y. Wu
  • E. Wulf
  • T. R. Wyatt
  • B. M. Wynne
  • S. Xella
  • M. Xiao
  • C. Xu
  • D. Xu
  • L. Xu
  • B. Yabsley
  • S. Yacoob
  • M. Yamada
  • H. Yamaguchi
  • Y. Yamaguchi
  • A. Yamamoto
  • K. Yamamoto
  • S. Yamamoto
  • T. Yamamura
  • T. Yamanaka
  • K. Yamauchi
  • Y. Yamazaki
  • Z. Yan
  • H. Yang
  • H. Yang
  • U. K. Yang
  • Y. Yang
  • S. Yanush
  • L. Yao
  • Y. Yasu
  • E. Yatsenko
  • K. H. Yau Wong
  • J. Ye
  • S. Ye
  • A. L. Yen
  • E. Yildirim
  • M. Yilmaz
  • R. Yoosoofmiya
  • K. Yorita
  • R. Yoshida
  • K. Yoshihara
  • C. Young
  • C. J. S. Young
  • S. Youssef
  • D. R. Yu
  • J. Yu
  • J. Yu
  • L. Yuan
  • A. Yurkewicz
  • B. Zabinski
  • R. Zaidan
  • A. M. Zaitsev
  • A. Zaman
  • S. Zambito
  • L. Zanello
  • D. Zanzi
  • A. Zaytsev
  • C. Zeitnitz
  • M. Zeman
  • A. Zemla
  • K. Zengel
  • O. Zenin
  • T. Ženiš
  • D. Zerwas
  • G. Zevi della Porta
  • D. Zhang
  • H. Zhang
  • J. Zhang
  • L. Zhang
  • X. Zhang
  • Z. Zhang
  • Z. Zhao
  • A. Zhemchugov
  • J. Zhong
  • B. Zhou
  • L. Zhou
  • N. Zhou
  • C. G. Zhu
  • H. Zhu
  • J. Zhu
  • Y. Zhu
  • X. Zhuang
  • A. Zibell
  • D. Zieminska
  • N. I. Zimine
  • C. Zimmermann
  • R. Zimmermann
  • S. Zimmermann
  • S. Zimmermann
  • Z. Zinonos
  • M. Ziolkowski
  • R. Zitoun
  • G. Zobernig
  • A. Zoccoli
  • M. zur Nedden
  • G. Zurzolo
  • V. Zutshi
  • L. Zwalinski
Open Access
Regular Article - Experimental Physics

Abstract

The jet energy scale (JES) and its systematic uncertainty are determined for jets measured with the ATLAS detector using proton–proton collision data with a centre-of-mass energy of \(\sqrt{s}=7\) TeV corresponding to an integrated luminosity of \(4.7\)\(\,\,\text{ fb }^{-1}\). Jets are reconstructed from energy deposits forming topological clusters of calorimeter cells using the anti-\(k_{t}\) algorithm with distance parameters \(R=0.4\) or \(R=0.6\), and are calibrated using MC simulations. A residual JES correction is applied to account for differences between data and MC simulations. This correction and its systematic uncertainty are estimated using a combination of in situ techniques exploiting the transverse momentum balance between a jet and a reference object such as a photon or a \(Z\) boson, for \({20} \le p_{\mathrm {T}}^\mathrm {jet}<{1000}\, ~\mathrm{GeV }\) and pseudorapidities \(|\eta |<{4.5}\). The effect of multiple proton–proton interactions is corrected for, and an uncertainty is evaluated using in situ techniques. The smallest JES uncertainty of less than 1 % is found in the central calorimeter region (\(|\eta |<{1.2}\)) for jets with \({55} \le p_{\mathrm {T}}^\mathrm {jet}<{500}\, ~\mathrm{GeV }\). For central jets at lower \(p_{\mathrm {T}}\), the uncertainty is about 3 %. A consistent JES estimate is found using measurements of the calorimeter response of single hadrons in proton–proton collisions and test-beam data, which also provide the estimate for \(p_{\mathrm {T}}^\mathrm {jet}> 1\) TeV. The calibration of forward jets is derived from dijet \(p_{\mathrm {T}}\) balance measurements. The resulting uncertainty reaches its largest value of 6 % for low-\(p_{\mathrm {T}}\) jets at \(|\eta |=4.5\). Additional JES uncertainties due to specific event topologies, such as close-by jets or selections of event samples with an enhanced content of jets originating from light quarks or gluons, are also discussed. The magnitude of these uncertainties depends on the event sample used in a given physics analysis, but typically amounts to 0.5–3 %.

1 Introduction

Jets are the dominant feature of high-energy, hard proton–proton interactions at the Large Hadron Collider (LHC) at CERN. They are key ingredients of many physics measurements and for searches for new phenomena. In this paper, jets are observed as groups of topologically related energy deposits in the ATLAS calorimeters, associated with tracks of charged particles as measured in the inner tracking detector. They are reconstructed with the anti-\(k_{t}\) jet algorithm [1] and are calibrated using Monte Carlo (MC) simulation.

A first estimate of the jet energy scale (JES) uncertainty of about 5–9 % depending on the jet transverse momentum (\(p_{\mathrm {T}}\)), described in Ref. [2], is based on information available before the first proton–proton collisions at the LHC, and initial proton–proton collision data taken in 2010. A reduced uncertainty of about 2.5 % in the central calorimeter region over a wide \(p_{\mathrm {T}}\) range of \(60 \lesssim p_{\mathrm {T}}< 800\) GeV was achieved after applying the increased knowledge of the detector performance obtained during the analysis of this first year of ATLAS data taking [3]. This estimation used single-hadron calorimeter response measurements, systematic variations of MC simulation configurations, and in situ techniques, where the jet transverse momentum is compared to the \(p_{\mathrm {T}}\) of a reference object. These measurements were performed using the 2010 dataset, corresponding to an integrated luminosity of 38 pb\(^{-1}\) [4].

During the year 2011 the ATLAS detector [5] collected proton–proton collision data at a centre-of-mass energy of \(\sqrt{s}=7\) TeV, corresponding to an integrated luminosity of about \(4.7\)\(\,\,\text{ fb }^{-1}\). The larger dataset makes it possible to further improve the precision of the jet energy measurement, and also to apply a correction derived from detailed comparisons of data and MC simulation using in situ techniques. This document presents the results of such an improved calibration of the jet energy measurement and the determination of the uncertainties using the 2011 dataset.

The energy measurement of jets produced in proton-proton and electron-proton collisions is also discussed by other experiments [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17].

The outline of the paper is as follows. Section 2 describes the ATLAS detector. The Monte Carlo simulation framework is presented in Sect. 3, and the used dataset is described in Sect. 4. Section 5 summarises the jet reconstruction and calibration strategy. The correction method for the effect of additional proton–proton interactions is discussed in Sect. 6. Section 7 provides an overview of the techniques based on \(p_{\mathrm {T}}\) balance that are described in detail in Sects. 8 to 11. First the intercalibration between the central and the forward detector using events with two high-\(p_{\mathrm {T}}\) jets is presented in Sect. 8. Then, in situ techniques to assess differences of the jet energy measurement between data and Monte Carlo simulation exploiting the \(p_{\mathrm {T}}\) balance between a jet and a well-measured reference object are detailed. The reference objects are \(Z\) bosons in Sect. 9, photons in Sect. 10, and a system of low-\(p_{\mathrm {T}}\) jets in Sect. 11. The validation of the forward-jet energy measurements with \(p_{\mathrm {T}}\) balance methods using \(Z\)-jet and \(\gamma \text {-jet}\) events follows in Sect. 12. The strategy on how to extract a final jet calibration out of the combination of in situ techniques, and the evaluation strategies for determining the corresponding systematic uncertainties, are discussed in Sect. 13. The same section also shows the final result of the jet calibration, including its systematic uncertainty, from the combination of the in situ techniques.

Section 14 compares the JES uncertainty as derived from the single-hadron calorimeter response measurements to that obtained from the in situ method based on \(p_{\mathrm {T}}\) balance discussed in the preceding sections. Comparisons to JES uncertainties using the \(W\) boson mass constraint in final states with hadronically decaying \(W\) bosons are presented in Sect. 15.

Additional contributions to the systematic uncertainties of the jet measurement in ATLAS are presented in Sects. 1618, where the correction for the effect of additional proton–proton interactions in the event, the presence of other close-by jets, and the response dependence on the jet fragmentation (jet flavour) are discussed. The uncertainties for explicitly tagged jets with heavy-flavour content are outlined in Sect. 19. A brief discussion of the correction of the calorimeter energy in regions with hardware failures and the associated uncertainty on the jet energy measurement is presented in Sect. 20.

A summary of the total jet energy scale uncertainty is given in Sect. 21. Conclusions follow in Sect. 22. A comparison of the systematic uncertainties of the JES in ATLAS with previous calibrations is presented in Appendix A.

2 The ATLAS detector

2.1 Detector description

The ATLAS detector consists of a tracking system (Inner Detector, or ID in the following), sampling electromagnetic and hadronic calorimeters and muon chambers. A detailed description of the ATLAS experiment can be found in Ref. [5].

The Inner Detector has complete azimuthal coverage and spans the pseudorapidity1 region \(|\eta |<2.5\). It consists of layers of silicon pixel detectors, silicon microstrip detectors and transition radiation tracking detectors, all of which are immersed in a solenoid magnet that provides a uniform magnetic field of 2 T.

Jets are reconstructed using the ATLAS calorimeters, whose granularity and material varies as a function of \(\eta \). The electromagnetic calorimetry (EM) is provided by high-granularity liquid-argon sampling calorimeters (LAr), using lead as an absorber. It is divided into one barrel (\(|\eta |<1.475\)) and two end-cap (\(1.375<|\eta |<3.2\)) regions. The hadronic calorimetry is divided into three distinct sections. The most central contains the central barrel region (\(|\eta |<0.8\)) and two extended barrel regions (\(0.8<|\eta |<1.7\)). These regions are instrumented with scintillator-tile/steel hadronic calorimeters (Tile). Each barrel region consists of 64 modules with individual \(\phi \) coverages of \(\sim0.1\) rad. The two hadronic end-cap calorimeters (HEC; \(1.5<|\eta |<3.2\)) feature liquid-argon/copper calorimeter modules. The two forward calorimeters (FCal; \(3.1<|\eta |<4.9\)) are instrumented with liquid-argon/copper and liquid-argon/tungsten modules to provide electromagnetic and hadronic energy measurements, respectively.

The muon spectrometer surrounds the ATLAS calorimeter. A system of three large air-core toroids, a barrel and two endcaps, generates a magnetic field in the pseudorapidity range of \(|\eta | < 2.7\). The muon spectrometer measures muon tracks with three layers of precision tracking chambers and is instrumented with separate trigger chambers.

The trigger system for the ATLAS detector consists of a hardware-based Level 1 (L1) and a software-based High Level Trigger (HLT) [18]. At L1, jets are first built from coarse-granularity calorimeter towers using a sliding window algorithm, and then subjected to early trigger decisions. This is refined using jets reconstructed from calorimeter cells in the HLT, with algorithms similar to the ones applied offline.

2.2 Calorimeter pile-up sensitivity

One important feature for the understanding of the contribution from additional proton–proton interactions (pile-up) to the signal in the 2011 dataset is the sensitivity of the ATLAS liquid argon calorimeters to the bunch crossing history. In any LAr calorimeter cell, the reconstructed energy is sensitive to the proton–proton interactions occurring in approximately 12 (2011 data, 24 at LHC design conditions) preceding and one immediately following bunch crossings (out-of-time pile-up), in addition to pile-up interactions in the current bunch crossing (in-time pile-up). This is due to the relatively long charge collection time in these calorimeters (typically 400–600 ns), as compared to the bunch crossing intervals at the LHC (design 25 ns and actually 50 ns in 2011 data). To reduce this sensitivity, a fast, bipolar shaped signal2 is used with net zero integral over time.

The signal shapes in the liquid argon calorimeters are optimised for this purpose, leading to cancellation on average of in-time and out-of-time pile-up in any given calorimeter cell. By design of the shaping amplifier, the most efficient suppression is achieved for 25 ns bunch spacing in the LHC beams. It is fully effective in the limit where, for each bunch crossing, about the same amount of energy is deposited in each calorimeter cell.

The 2011 beam conditions, with 50 ns bunch spacing and a relatively low cell occupancy from the achieved instantaneous luminosities, do not allow for full pile-up suppression by signal shaping, in particular in the central calorimeter region. Pile-up suppression is further limited by large fluctuations in the number of additional interactions from bunch crossing to bunch crossing, and in the energy flow patterns of the individual collisions in the time window of sensitivity of approximately 600 ns. Consequently, the shaped signal extracted by digital filtering shows a principal sensitivity to in-time and out-of-time pile-up, in particular in terms of a residual non-zero cell-signal baseline. This baseline can lead to relevant signal offsets once the noise suppression, an important part of the calorimeter signal extraction strategy presented in Sect. 5, is applied.

Corrections mitigating the effect of these signal offsets on the reconstructed jet energy are discussed in the context of the pile-up suppression strategy in Sect. 6.1. All details of the ATLAS liquid argon calorimeter readout and signal processing can be found in Ref. [19].

The Tile calorimeter shows very little sensitivity to pile-up since most of the associated (soft particle) energy flow is absorbed in the LAr calorimeters in front of it. Moreover, out-of-time pile-up is suppressed by a short shaping time with sensitivity to only about 3 bunch crossings [20].

3 Monte Carlo simulation of jets in the ATLAS detector

The energy and direction of particles produced in proton–proton collisions are simulated using various MC event generators. An overview of these generators for LHC physics can be found in Ref. [21]. The samples using different event generators and theoretical models are described below. All samples are produced at \(\sqrt{s}= 7\) TeV.

3.1 Inclusive jet Monte Carlo simulation samples

  1. 1.

    Pythia (version 6.425) [22] is used for the generation of the baseline simulation event samples. It models the hard sub-process in the final states of the generated proton–proton collisions using a \(2 \rightarrow 2\) matrix element at leading order in the strong coupling \(\alpha _{\mathrm {S}}\). Additional radiation is modelled in the leading logarithmic (LL) approximation by \(p_{\mathrm {T}}\)-ordered parton showers [23]. Multiple parton interactions (MPI) [24], as well as fragmentation and hadronisation based on the Lund string model [25], are also generated. Relevant parameters for the modelling of the parton shower and multiple parton interactions in the underlying event (UE) are tuned to LHC data (ATLAS Pythia tune AUET2B [26] with the MRST LO** parton density function (PDF) [27]). Data from the LEP collider are included in this tune.

     
  2. 2.

    Herwig++ [28] is used to generate samples for evaluating systematic uncertainties. This generator uses a \(2 \rightarrow 2\) matrix element and angular-ordered parton showers in the LL approximation [29, 30, 31]. The cluster model [32] is employed for the hadronisation. The underlying event and soft inclusive interactions are described using a hard and soft MPI model [33]. The parton densities are provided by the MRST LO** PDF set.

     
  3. 3.

    MadGraph [34] with the CTEQ6L1 PDF set [35] is used to generate proton–proton collision samples with up to three outgoing partons from the matrix element and with MLM matching [36] applied in the parton shower, which is performed with Pythia using the AUET2B tune.

     

3.2 Z-jet and \({\gamma }\)-jet Monte Carlo simulation samples

  1. 1.

    Pythia (version 6.425) is used to produce \(Z\)-jet events with the modified leading-order PDF set MRST LO**. The simulation uses a \(2 \rightarrow 1\) matrix element to model the hard sub-process, and, as for the inclusive jet simulation, \(p_{\mathrm {T}}\)-ordered parton showers to model additional parton radiation in the LL approximation. In addition, weights are applied to the first branching of the shower, so as to bring agreement with the matrix-element rate in the hard emission region. The same tune and PDF is used as for the inclusive jet sample.

     
  2. 2.

    The Alpgen generator (version 2.13) [37] is used to produce \(Z\)-jet events, interfaced to Herwig (version 6.510) [31] for parton shower and fragmentation into particles, and to Jimmy (version 4.31) [38] to model UE contributions using the ATLAS AUET2 tune [39], here with the CTEQ6L1 [35] leading-order PDF set. Alpgen is a leading-order matrix-element generator for hard multi-parton processes (\(2\rightarrow n\)) in hadronic collisions. Parton showers are matched to the matrix element with the MLM matching scheme. The CTEQ6L1 PDF set is employed.

     
  3. 3.

    The baseline \(\gamma \text {-jet}\) sample is produced with Pythia (version 6.425). It generates non-diffractive events using a \(2 \rightarrow 2\) matrix element at leading order in \(\alpha _{\mathrm {S}}\) to model the hard sub-process. Again, additional parton radiation is modelled by \(p_{\mathrm {T}}\)-ordered parton showers in the LL approximation. The modelling of non-perturbative physics effects arising in MPI, fragmentation, and hadronisation is based on the ATLAS AUET2BMRSTLO** tune.

     
  4. 4.

    An alternative \(\gamma \text {-jet}\) event sample is generated with Herwig (version 6.510) and Jimmy using the ATLAS AUET2 tune and the MRSTLO** PDF. It is used to evaluate the systematic uncertainty due to physics modelling.

     
  5. 5.

    The systematic uncertainty from jets which are misidentified as photons (fake photons) is studied with a dedicated MC event sample. An inclusive jet sample is generated with Pythia (version 6.425) with the same parameter tuning and PDF set as the \(\gamma \text {-jet}\) sample. An additional filter is applied to the jets built from the stable generated particles to select events containing a narrow particle jet, which is more likely to pass photon identification criteria. The surviving events are passed through the same detector simulation software as the MC \(\gamma \text {-jet}\) sample.

     

3.3 Top-quark pair Monte Carlo simulation samples

Top pair (\(t\bar{t}\)) production samples are relevant for jet reconstruction performance studies, as they are a significant source of hadronically decaying \(W\) bosons and therefore important for light-quark jet response evaluations in a radiation environment very different from the inclusive jet and \(Z\)-jet/\(\gamma \text {-jet}\) samples discussed above. In addition, they provide jets from a heavy-flavour (\(b\)-quark) decay, the response to which can be studied in this final state as well.

The nominal \(t\bar{t}\) event sample is generated using MC@NLO (version 4.01) [40], which implements a next-to-leading-order (NLO) matrix element for top-pair production. Correspondingly, the CT10 [41] NLO PDF set is used. This matrix-element generator is interfaced to parton showers from Herwig (version 6.520) [42] and the underlying event modelled by Jimmy (version 4.31), with the CT10 PDF and the ATLAS AUET2 tune.

A number of systematic variation samples use alternative MC generators or different generator parameter sets. Additional \(t\bar{t}\) samples are simulated using the POWHEG [43] generator interfaced with Pythia, as well as Herwig and Jimmy. POWHEG provides alternative implementations of the NLO matrix-element calculation and the interface to parton showers. These samples allow comparison of two different parton shower, hadronisation and fragmentation models. In addition, the particular implementations of the NLO matrix-element calculations in POWHEG and MC@NLO can be compared. Differences in the \(b\)-hadron decay tables between Pythia and Herwig are also significant enough to provide a conservative uncertainty envelope on the effects of the decay model.

In addition, samples with more or less parton shower activity are generated with the leading-order generator ACERMC [44] interfaced to Pythia with the MRST LO** PDF set. These are used to estimate the model dependence of the event selection. In these samples the initial state radiation (ISR) and the final state radiation (FSR) parameters are varied in value ranges not excluded by the current experimental data, as detailed in Refs. [45, 46].
Fig. 1

The energy-equivalent cell noise in the ATLAS calorimeters on the electromagnetic (EM) scale as a function of the direction \(|\eta |\) in the detector, for the 2010 configuration with a\(\mu = 0\) and the 2011 configuration with b\(\mu = 8\). The various colours indicate the noise in the pre-sampler (PS) and the up to three layers of the LArEM calorimeter, the up to three layers of the Tile calorimeter, the four layers for the hadronic end-cap (HEC) calorimeter, and the three modules of the forward (FCal) calorimeter

3.4 Minimum bias samples

Minimum bias events are generated using Pythia8 [47] with the 4C tune [48] and MRST LO** PDF set. These minimum bias events are used to form pile-up events, which are overlaid onto the hard-scatter events following a Poisson distribution around the average number \(\langle \mu \rangle \) of additional proton–proton collisions per bunch crossing measured in the experiment. The LHC bunch train structure with 36 proton bunches per train and 50 ns spacing between the bunches, is also modelled by organising the simulated collisions into four such trains. This allows the inclusion of out-of-time pile-up effects driven by the distance of the hard-scatter events from the beginning of the bunch train. The first ten bunch crossings in each LHC bunch train, approximately, are characterised by varying out-of-time pile-up contributions from the collision history, which is getting filled with an increasing number of bunch crossings with proton–proton interactions. For the remaining \(\approx \)26 bunch crossings in a train, the effect of the out-of-time pile-up contribution is stable, i.e. it does not vary with the bunch position within the bunch train, if the bunch-to-bunch intensity is constant. Bunch-to-bunch fluctuations in proton intensity at the LHC are not included in the simulation.

3.5 Detector simulation

The Geant4 software toolkit [49] within the ATLAS simulation framework [50] propagates the stable particles3 produced by the event generators through the ATLAS detector and simulates their interactions with the detector material. Hadronic showers are simulated with the QGSP_BERT model [51, 52, 53, 54, 55, 56, 57, 58, 59]. Compared to the simulation used in the context of the 2010 data analysis, a newer version of Geant4 (version 9.4) is used and a more detailed description of the geometry of the LAr calorimeter absorber structure is available. These geometry changes introduce an increase in the calorimeter response to pions below 10 GeV of about 2 %.

For the estimation of the systematic uncertainties arising from detector simulation, several samples are also produced with the ATLAS fast (parameterised) detector simulation ATLFAST2 [50, 60].

4 Dataset

The data used in this study were recorded by ATLAS between May and October 2011, with all ATLAS subdetectors operational. The corresponding total integrated luminosity is about \(4.7\)\(\,\,\text{ fb }^{-1}\) of proton–proton collisions at a centre-of-mass energy of \(\sqrt{s}=7\) TeV.

As already indicated in Sect. 3.4, the LHC operated with bunch crossing intervals of 50 ns, and bunches organised in bunch trains. The average number of interactions per bunch crossing (\(\mu \)) as estimated from the luminosity measurement is \(3 \le \mu \le 8\) until Summer 2011, with an average for this period of \(\langle \mu \rangle \approx 6\). Between August 2011 and the end of the proton run, \(\mu \) increased to about \(5 \le \mu \le 17\), with an average \(\langle \mu \rangle \approx 12\). The average number of interactions for the whole 2011 dataset is \(\langle \mu \rangle = 8\).

The specific trigger requirements and precision signal object selections applied to the data are analysis dependent. They are therefore discussed in the context of each analysis presented in this paper.

5 Jet reconstruction and calibration with the ATLAS detector

5.1 Topological clusters in the calorimeter

Clusters of energy deposits in the calorimeter (topo-clusters) are built from topologically connected calorimeter cells that contain a significant signal above noise, see Refs. [3, 61, 62] for details. The topo-cluster formation follows cell signal significance patterns in the ATLAS calorimeters. The signal significance is measured by the absolute ratio of the cell signal to the energy-equivalent noise in the cell. The signal-to-noise thresholds for the cluster formation are not changed with respect to the settings given in Ref. [3]. However, the noise in the calorimeter increased due to the presence of multiple proton-proton interactions, as discussed in Sect. 2.2, and required the adjustments explained below.

While in ATLAS operations prior to 2011 the cell noise was dominated by electronic noise, the short bunch crossing interval in 2011 LHC running added a noise component from bunch-to-bunch variations in the instantaneous luminosity and in the energy deposited in a given cell from previous collisions inside the window of sensitivity of the calorimeters. The cell noise thresholds steering the topo-cluster formation thus needed to be increased from those used in 2010 to accommodate the corresponding fluctuations, which is done by raising the nominal noise according to
$$\begin{aligned} \sigma _{\text {noise}}= \left\{ \begin{array}{l@{\quad }l} \sigma _{\text {noise}}^{\text {electronic}}&{} \mathrm{(2010~operations)} \\ \sqrt{\left( \sigma _{\text {noise}}^{\text {electronic}}\right) ^{2} + \left( \sigma _{\text {noise}}^{\text {pile-up}}\right) ^{2}}&{}\mathrm{(2011~operations)} \end{array}\right. . \end{aligned}$$
Here, \(\sigma _{\text {noise}}^{\text {electronic}}\) is the electronic noise, and \(\sigma _{\text {noise}}^{\text {pile-up}}\) the noise from pile-up, determined with MC simulations and corresponding to an average of eight additional proton–proton interactions per bunch crossing (\(\mu = 8\)) in 2011. The change of the total nominal noise \(\sigma _{\text {noise}}\) and its dependence on the calorimeter region in ATLAS can be seen by comparing Fig. 1a and b. In most calorimeter regions, the noise induced by pile-up is smaller than or of the same magnitude as the electronic noise, with the exception of the forward calorimeters, where \(\sigma _{\text {noise}}^{\text {pile-up}}\gg \sigma _{\text {noise}}^{\text {electronic}}\).
The implicit noise suppression implemented by the topological cluster algorithm discussed above leads to significant improvements in the calorimeter performance for e.g. the energy and spatial resolutions in the presence of pile-up. On the other hand, contributions from larger negative and positive signal fluctuations introduced by pile-up can survive in a given event. They thus contribute to the sensitivity to pile-up observed in the jet response, in addition to the cell-level effects mentioned in Sect. 2.2.
Fig. 2

Overview of the ATLAS jet reconstruction. After the jet finding, the jet four momentum is defined as the four momentum sum of its constituents

Fig. 3

Overview of the ATLAS jet calibration scheme used for the 2011 dataset. The pile-up, absolute JES and the residual in situ corrections calibrate the scale of the jet, while the origin and the \(\eta \) corrections affect the direction of the jet

5.2 Jet reconstruction and calibration

Jets are reconstructed using the anti-\(k_{t}\) algorithm [1] with distance parameters \(R = 0.4\) or \(R = 0.6\), utilising the FastJet software package [63, 64]. The four-momentum scheme is used at each recombination step in the jet clustering. The total jet four-momentum is therefore defined as the sum of the four-momenta sum of all its constituents. The inputs to the jet algorithm are stable simulated particles (truth jets, see Sect. 5.5 for details), reconstructed tracks in the inner detector (track jets, see Ref. [3] and Sect. 5.4 for details) or energy deposits in the calorimeter (calorimeter jets, see below for details). A schematic overview of the ATLAS jet reconstruction is presented in Fig. 2.

The calorimeter jets are built from the topo-clusters entering as massless particles in the jet algorithm as discussed in the previous section. Only clusters with positive energy are considered. The topo-clusters are initially reconstructed at the EM scale [61, 65, 66, 67, 68, 69, 70, 71, 72], which correctly measures the energy deposited in the calorimeter by particles produced in electromagnetic showers. A second topo-cluster collection is built by calibrating the calorimeter cell such that the response of the calorimeter to hadrons is correctly reconstructed. This calibration uses the local cell signal weighting (LCW) method that aims at an improved resolution compared to the EM scale by correcting the signals from hadronic deposits, and thus reduces fluctuations due to the non-compensating nature of the ATLAS calorimeter. The LCW method first classifies topo-clusters as either electromagnetic or hadronic, primarily based on the measured energy density and the longitudinal shower depth. Energy corrections are derived according to this classification from single charged and neutral pion MC simulations. Dedicated corrections address effects of calorimeter non-compensation, signal losses due to noise threshold effects, and energy lost in non-instrumented regions close to the cluster [3].
Fig. 4

Average response of simulated jets formed from topo-clusters, calculated as defined in Eq. (1) and shown in a for the EM scale (\(\mathfrak {R}^{\mathrm{EM}}\)) and in b for the LCW scale (\(\mathfrak {R}^{\mathrm{LCW}}\)). The response is shown separately for various truth-jet energies as function of the uncorrected (detector) jet pseudorapidity \(\eta _\mathrm{det}\). Also indicated are the different calorimeter regions. The inverse of \(\mathfrak {R}^{\mathrm{EM}}\) (\(\mathfrak {R}^{\mathrm{LCW}}\)) corresponds to the average jet energy scale correction for EM (LCW) in each \(\eta _\mathrm{det}\) bin. The results shown are based on the baseline Pythia inclusive jet sample

Figure 3 shows an overview of the ATLAS calibration scheme for calorimeter jets used for the 2011 dataset, which restores the jet energy scale to that of jets reconstructed from stable simulated particles (truth particle level, see Sect. 5.5). This procedure consists of four steps as described below.
  1. 1.

    Pile-up correction Jets formed from topo-clusters at the EM or LCW scale are first calibrated by applying a correction to account for the energy offset caused by pile-up interactions. The effects of pile-up on the jet energy scale are caused by both additional proton collisions in a recorded event (in-time pile-up) and by past and future collisions influencing the energy deposited in the current bunch-crossing (out-of-time pile-up), and are outlined in Sect. 6. This correction is derived from MC simulations as a function of the number of reconstructed primary vertices (\(N_{\mathrm{PV}}\), measuring the actual collisions in a given event) and the expected average number of interactions (\(\mu \), sensitive to out-of-time pile-up) in bins of jet pseudorapidity and transverse momentum (see Sect. 6).

     
  2. 2.

    Origin correction A correction to the calorimeter jet direction is applied that makes the jet pointing back to the primary event vertex instead of the nominal centre of the ATLAS detector.

     
  3. 3.
    Jet calibration based on MC simulations Following the strategy presented in Ref. [3], the calibration of the energy and pseudorapidity of a reconstructed jet is a simple correction derived from the relation of these quantities to the corresponding ones of the matching truth jet (see Sect. 5.5) in MC simulations. It can be applied to jets formed from topo-clusters at EM or at LCW scale with the resulting jets being referred to as calibrated with the EM+JES or with the LCW+JES scheme. This first JES correction uses isolated jets from an inclusive jet MC sample including pile-up events (the baseline sample described in Sect. 3). Figure 4 shows the average energy response
    $$\begin{aligned} \mathfrak {R}^{\mathrm{EM}(\mathrm{LCW})}=E^{\mathrm{EM}(\mathrm{LCW})}_\mathrm{jet}/E^\mathrm{truth}_\mathrm{jet}, \end{aligned}$$
    (1)
    which is the inverse of the jet energy calibration function, for various jet energies as a function of the jet pseudorapidity \(\eta _\mathrm{det}\) measured in the detector frame of reference (see Sect. 5.6).
     
  4. 4.

    Residual in situ corrections A residual correction derived in situ is applied as a last step to jets reconstructed in data. The derivation of this correction is described in Sect. 7.

     
Fig. 5

Jet quality selection efficiency for anti-\(k_{t}\) jets with \(R = 0.4\) measured with a tag-and-probe technique as a function of \(p_{\mathrm {T}}^\mathrm {jet}\) in various \(\eta \) ranges, for the four sets of selection criteria. Only statistical uncertainties are shown. Differences between data and MC simulations are also shown

5.3 Jet quality selection

Jets with high transverse momenta produced in proton–proton collisions must be distinguished from background jet candidates not originating from hard-scattering events. A first strategy to select jets from collisions and to suppress background is presented in Ref. [3].

The main sources of potential background are:
  1. 1.

    Beam-gas events, where one proton of the beam collides with the residual gas within the beam pipe.

     
  2. 2.

    Beam-halo events, for example caused by interactions in the tertiary collimators in the beam-line far away from the ATLAS detector.

     
  3. 3.

    Cosmic-ray muons overlapping in-time with collision events.

     
  4. 4.

    Calorimeter noise.

     
The jet quality selection criteria should efficiently reject jets from these background processes while maintaining high efficiency for selecting jets produced in proton–proton collisions. Since the level and composition of background depend on the event topology and the jet kinematics, four sets of criteria called Looser, Loose, Medium and Tight are introduced in Ref. [73]. They correspond to different levels of fake-jet rejection and jet selection efficiency, with the Looser criterion being the one with the highest jet selection efficiency while the Tight criterion is the one with the best rejection. The discrimination between jets coming from the collisions and background jet candidates is based on several pieces of experimental information, including the quality of the energy reconstruction at the cell level, jet energy deposits in the direction of the shower development, and reconstructed tracks matched to the jets.

The efficiencies of the jet selection criteria are measured using the tag-and-probe method described in Ref. [3]. The resulting efficiencies for anti-\(k_{t}\) jets with \(R = 0.4\) for all selection criteria are shown in Fig. 5. The jet selection efficiency of the Looser selection is greater than 99.8 % over all calibrated transverse jet momenta \(p_{\mathrm {T}}^\mathrm {jet}\) and \(\eta \) bins. A slightly lower efficiency of about 1–2 % is measured for the Loose selection, in particular at low \(p_{\mathrm {T}}^\mathrm {jet}\) and for \(2.5 <|\eta |< 3.6\). The Medium and Tight selections have lower jet selection efficiencies mainly due to cuts on the jet charged fraction, which is the ratio of the scalar sum of the \(p_{\mathrm {T}}\) of all reconstructed tracks matching the jet, and the jet \(p_{\mathrm {T}}\) itself, see Ref. [73] for more details. For jets with \(p_{\mathrm {T}}^\mathrm {jet}\approx 25\) GeV, the Medium and Tight selections have inefficiencies of 4 and 15 %, respectively. For \(p_{\mathrm {T}}^\mathrm {jet}> 50\) GeV, the Medium and Tight selections have efficiencies greater than 99 and 98 %, respectively.

The event selection is based on the azimuthal distance between the probe and tag jet \(\Delta \phi (\mathrm {tag},\mathrm {probe})\) and the significance of the missing transverse momentum \({E}_{\mathrm {T}}^{\mathrm {miss}}\) [74] reconstructed for the event, which is measured by the ratio \({E}_{\mathrm {T}}^{\mathrm {miss}}/\sqrt{\Sigma E_{\mathrm {T}}}\). Here \(\Sigma E_{\mathrm {T}}\) is the scalar transverse momentum sum of all particles, jets, and soft signals in the event. The angle \(\Delta \phi (\mathrm {tag},\mathrm {probe})\), \({E}_{\mathrm {T}}^{\mathrm {miss}}/\sqrt{\Sigma E_{\mathrm {T}}}\), and the Tight selection of the reference (tag) jet are varied to study the systematic uncertainties. For the Loose and Looser selections, the jet selection efficiency is almost unchanged by varying the selection cuts, with variations of less than 0.05 %. Slightly larger changes are observed for the two other selections, but they are not larger than 0.1 % for the Medium and 0.5 % for the Tight selection.

The jet selection efficiency is also measured using a MC simulation sample. A very good agreement between data and simulation is observed for the Looser and Loose selections. Differences not larger than 0.2 and 1 % are observed for the Medium and Tight selections, respectively, for \(p_{\mathrm {T}}^\mathrm {jet}> 40\) GeV. Larger differences are observed at lower \(p_{\mathrm {T}}^\mathrm {jet}\), but they do not exceed 1 % (2 %) for the Medium(Tight) selection.

5.4 Track jets

In addition to the previously described calorimeter jets reconstructed from topo-clusters, track jets in ATLAS are built from reconstructed charged particle tracks associated with the reconstructed primary collision vertex, which is defined by
$$\begin{aligned} \sum (p_{\mathrm {T}}^{\mathrm {track}})^{2} = \max . \end{aligned}$$
Here \(p_{\mathrm {T}}^{\mathrm {track}}\) is the transverse momentum of tracks pointing to a given vertex. The tracks associated with the primary vertex are required to have \(p_{\mathrm {T}}^{\mathrm {track}}> 500\)  MeV and to be within \(|\eta | < 2.5\). Additional reconstruction quality criteria are applied, including the number of hits in the pixel detector (at least one) and in the silicon microstrip detector (at least six) of the ATLAS ID system. Further track selections are based on the transverse (\(d_{0}\), perpendicular to the beam axis) and longitudinal (\(z_{0}\), along the beam axis) impact parameters of the tracks measured with respect to the primary vertex (\(|d_{0}|<1.5\) mm, \(|z_{0}\sin \theta |<1.5\) mm). Here \(\theta \) is the polar angle of the track.

Generally, track jets used in the studies presented in this paper are reconstructed with the same configurations as calorimeter jets, i.e. using the anti-\(k_{t}\) algorithm with \(R = 0.4\) and \(R = 0.6\). As only tracks originating from the hardest primary vertex in the collision event are used in the jet finding, the transverse momentum of any of these track jets provides a rather stable kinematic reference for matching calorimeter jets, as it is independent of the pile-up activity. Track jets can of course only be formed within the tracking detector coverage (\(|\eta |<{2.5}\)), yielding an effective acceptance for track jets of \(|\eta _{\mathrm {track jet}}| < 2.5 - R\).

Certain studies may require slight modifications of the track selection and the track-jet formation criteria and algorithms. Those are indicated in the respective descriptions of the applied methods. In particular, track jets may be further selected by requirements concerning the number of clustered tracks, the track-jet \(p_{\mathrm {T}}\), and the track-jet direction.

5.5 Truth jets

Truth jets can be formed from stable particles generated in MC simulations. In general those are particles with a lifetime \(\tau \) defined by \(c \tau > 10\) mm [75]. The jet definitions applied are the same as the ones used for calorimeter and track jets (anti-\(k_{t}\) with distance parameters \(R = 0.4\) and \(R = 0.6\), respectively). If truth jets are employed as a reference for calibrations purposes in MC simulations, neither final-state muons nor neutrinos are included in the stable particles considered for its formation. The simulated calorimeter jets are calibrated with respect to truth jets consisting of stable particles leaving an observable signal (visible energy) in the detector.4 This is a particular useful strategy for inclusive jet measurements and the universal jet calibration discussed in this paper, but special truth-jet references including muons and/or neutrinos may be utilised as well, in particular to understand the heavy-flavour jet response, as discussed in detail in Sect. 19.

5.6 Jet kinematics and directions

Kinematic properties of jets relevant for their use in final-state selections and final-state reconstruction are the transverse momentum \(p_{\mathrm {T}}\) and the rapidity \(y\). The full reconstruction of the jet kinematics including these variables takes into account the physics frame of reference, which in ATLAS is defined event-by-event by the primary collision vertex discussed in Sect. 5.4.

On the other hand, many effects corrected by the various JES calibrations discussed in this paper are highly localised, i.e. they are due to specific detector features and inefficiencies at certain directions or ranges. The relevant directional variable to use as a basis for these corrections is then the detector pseudorapidity \(\eta _\mathrm{det}\), which is reconstructed in the nominal detector frame of reference in ATLAS, and is centred at the nominal collision vertex \((x = 0,y = 0,z=0)\).

Directional relations to jets, and e.g. between the constituents of jet and its principal axis, can then be measured either in the physics or the detector reference frame, with the choice depending on the analysis. In the physics reference frame ((\(y,\phi \)) space) the distance between any two objects is given by
$$\begin{aligned} \Delta R= \sqrt{(\Delta y)^{2} + (\Delta \phi )^{2}} , \end{aligned}$$
(2)
where \(\Delta y\) is the rapidity distance and \(\Delta \phi \) is the azimuthal distance between them. The same distance measured in the detector frame of reference ((\(\eta ,\phi \)) space) is calculated as
$$\begin{aligned} \Delta R= \sqrt{(\Delta \eta )^{2} + (\Delta \phi )^{2}} , \end{aligned}$$
(3)
where \(\Delta \eta \) is the distance in pseudorapidity between any two objects. In case of jets and their constituents (topo-clusters or tracks), \(\eta = \eta _\mathrm{det}\) is used. All jet clustering algorithms used in ATLAS apply the physics frame distance in Eq. (2) in their distance evaluations, as jets are considered to be massive physical objects, and the jet clustering is intended to follow energy flow patterns introduced by the physics of parton showers, fragmentation, and hadronisation from a common (particle) source. In this context topo-clusters and reconstructed tracks are considered pseudo-particles representing the true particle flow within the limitations introduced by the respective detector acceptances and resolutions.
Fig. 6

The average reconstructed transverse momentum \(p_\mathrm{T, \mathrm{EM}}^\mathrm {jet}\) on EM scale for jets in MC simulations, as function of the number of reconstructed primary vertices \(N_{\mathrm{PV}}\) and \(7.5 \le \mu < 8.5\), in various bins of truth-jet transverse momentum \(p_{\mathrm {T}}^\mathrm {truth}\), for jets with a\(R =0.4\) and b\(R = 0.6\). The dependence of \(p_\mathrm{T, \mathrm{EM}}^\mathrm {jet}\) on \(N_{\mathrm{PV}}\) in data, in bins of track-jet transverse momentum \(p_{\mathrm {T}}^{\mathrm {track}}\), is shown in c for \(R = 0.4\) jets, and in d for \(R = 0.6\) jets

6 Jet energy correction for pile-up interactions

6.1 Pile-up correction method

The pile-up correction method applied to reconstructed jets in ATLAS is derived from MC simulations and validated with in situ and simulation based techniques. The approach is to calculate the amount of transverse momentum generated by pile-up in a jet in MC simulation, and subtract this offset \(\mathcal {O}\) from the reconstructed jet \(p_{\mathrm {T}}^{\mathrm {jet}}\) at any given signal scale (EM or LCW). At least to first order, pile-up contributions to the jet signal can be considered stochastic and diffuse with respect to the true jet signal. Therefore, both in-time and out-of-time pile-up are expected to depend only on the past and present pile-up activity, with linear relations between the amount of activity and the pile-up signal.

6.2 Principal pile-up correction strategy

To characterise the in-time pile-up activity, the number of reconstructed primary vertices (\(N_{\mathrm{PV}}\)) is used. The ATLAS tracking detector timing resolution allows the reconstruction of only in-time tracks and vertices, so that \(N_{\mathrm{PV}}\) provides a good measure of the actual number of proton–proton collisions in a recorded event.

For the out-of-time pile-up activity, the average number of interactions per bunch crossing (\(\mu \)) at the time of the recorded events provides a good estimator. It is derived by averaging the actual number of interactions per bunch crossing over a rather large window \(\Delta t\) in time, which safely encompasses the time interval during which the ATLAS calorimeter signal is sensitive to the activity in the collision history (\(\Delta t \gg 600\) ns for the liquid-argon calorimeters). The observable \(\mu \) can be reconstructed from the average luminosity \(L\) over this period \(\Delta t\), the total inelastic proton–proton cross section (\(\sigma _{\mathrm {inel}} = 71.5\) mb [76]), the number of colliding bunches in LHC (\(N_{\mathrm {bunch}}\)) and the LHC revolution frequency (\(f_{\mathrm {LHC}}\)) (see Ref. [77] for details):
$$\begin{aligned} \mu = \frac{L \times \sigma _{\mathrm {inel}}}{N_{\mathrm {bunch}} \times f_{\mathrm {LHC}}} . \end{aligned}$$
The MC-based jet calibration is derived for a given (reference) pile-up condition5\((N_{\mathrm{PV}}^\mathrm{ref},\mu ^{\mathrm {ref}})\) such that \(\mathcal {O}(N_{\mathrm{PV}}= N_{\mathrm{PV}}^\mathrm{ref},\)\(\mu = \mu ^{\mathrm {ref}}) = 0\). As the amount of energy scattered into a jet by pile-up and the signal modification imposed by the pile-up history determine \(\mathcal {O}\), a general dependence on the distances from the reference point is expected. From the nature of pile-up discussed earlier, the linear scaling of \(\mathcal {O}\) in both \(N_{\mathrm{PV}}\) and \(\mu \) provides the ansatz for a correction,
$$\begin{aligned}&\mathcal {O}(N_{\mathrm{PV}},\mu ,\eta _\mathrm{det}) = p_{\mathrm {T}}^{\mathrm {jet}}(N_{\mathrm{PV}},\mu ,\eta _\mathrm{det}) - p_{\mathrm {T}}^\mathrm {truth}\nonumber \\&\quad = \frac{\partial p_{\mathrm {T}}}{\partial N_{\mathrm{PV}}}(\eta _\mathrm{det}) \left( N_{\mathrm{PV}}- N_{\mathrm{PV}}^\mathrm{ref}\right) + \frac{\partial p_{\mathrm {T}}}{\partial {\mu }}(\eta _\mathrm{det}) \left( \mu - \mu ^{\mathrm {ref}}\right) \nonumber \\&\quad = \alpha (\eta _\mathrm{det})\cdot \left( N_{\mathrm{PV}}- N_{\mathrm{PV}}^\mathrm{ref}\right) + \beta (\eta _\mathrm{det})\cdot \left( \mu - \mu ^{\mathrm {ref}}\right) \end{aligned}$$
(4)
Here, \(p_{\mathrm {T}}^{\mathrm {jet}}(N_{\mathrm{PV}},\mu ,\eta _\mathrm{det}) \) is the reconstructed transverse momentum of the jet (without the JES correction described in Sect. 5.2 applied) in a given pile-up condition (\(N_{\mathrm{PV}}\),\(\mu \)) and at a given direction \(\eta _\mathrm{det}\) in the detector. The true transverse momentum of the jet (\(p_{\mathrm {T}}^\mathrm {truth}\)) is available from the generated particle jet matching a reconstructed jet in MC simulations. The coefficients \(\alpha (\eta _\mathrm{det})\) and \(\beta (\eta _\mathrm{det})\) depend on \(\eta _\mathrm{det}\), as both in-time and out-of-time pile-up signal contributions manifest themselves differently in different calorimeter regions, according to the following influences:
  1. 1.

    The energy flow from collisions into that region.

     
  2. 2.

    The calorimeter granularity and occupancy after topo-cluster reconstruction, leading to different acceptances at cluster level and different probabilities for multiple particle showers to overlap in a single cluster.

     
  3. 3.

    The effective sensitivity to out-of-time pile-up introduced by different calorimeter signal shapes.

     
The offset \(\mathcal {O}\) can be determined in MC simulation for jets on the EM or the LCW scale by using the corresponding reconstructed transverse momentum on one of those scales, i.e. \(p_{\mathrm {T}}^{\mathrm {jet}} = p_{\mathrm {T,\mathrm{EM}}}^{\mathrm {jet}}\) or \(p_{\mathrm {T}}^{\mathrm {jet}} = p_{\mathrm {T,\mathrm{LCW}}}^{\mathrm {jet}}\) in Eq. (4), and \(p_{\mathrm {T}}^\mathrm {truth}\). The particular choice of scale affects the magnitude of the coefficients and, therefore, the transverse momentum offset itself,
$$\begin{aligned}&\mathcal {O}^{\mathrm{EM}} \mapsto \left\{ \alpha ^{\mathrm{EM}}(\eta _\mathrm{det}),\beta ^{\mathrm{EM}}(\eta _\mathrm{det})\right\} \\&\mathcal {O}^{\mathrm{LCW}} \mapsto \left\{ \alpha ^{\mathrm{LCW}}(\eta _\mathrm{det}),\beta ^{\mathrm{LCW}}(\eta _\mathrm{det})\right\} . \end{aligned}$$
The corrected transverse momentum of the jet at either of the two scales (\(p_{\mathrm {T,\mathrm{EM}}}^{\mathrm {corr}}\) or \(p_{\mathrm {T,\mathrm{LCW}}}^{\mathrm {corr}}\)) is then given by
$$\begin{aligned}&p_{\mathrm {T,\mathrm{EM}}}^{\mathrm {corr}} = p_{\mathrm {T,\mathrm{EM}}}^{\mathrm {jet}} - \mathcal {O}^{\mathrm{EM}}(N_{\mathrm{PV}},\mu ,\eta _\mathrm{det}) \end{aligned}$$
(5)
$$\begin{aligned}&p_{\mathrm {T,\mathrm{LCW}}}^{\mathrm {corr}} = p_{\mathrm {T,\mathrm{LCW}}}^{\mathrm {jet}} - \mathcal {O}^{\mathrm{LCW}}(N_{\mathrm{PV}},\mu ,\eta _\mathrm{det}) . \end{aligned}$$
(6)
After applying the correction, the original \(p_{\mathrm {T,\mathrm{EM}}}^{\mathrm {jet}}\) and \(p_{\mathrm {T,\mathrm{LCW}}}^{\mathrm {jet}}\) dependence on \(N_{\mathrm{PV}}\) and \(\mu \) is expected to vanish in the corresponding corrected \(p_{\mathrm {T,\mathrm{EM}}}^{\mathrm {corr}}\) and \(p_{\mathrm {T,\mathrm{LCW}}}^{\mathrm {corr}}\).
Fig. 7

The average reconstructed jet transverse momentum \(p_\mathrm{T, \mathrm{EM}}^\mathrm {jet}\) on EM scale as function of the average number of collisions \(\mu \) at a fixed number of primary vertices \(N_{\mathrm{PV}}= 6\), for truth jets in MC simulation a in the lowest bin of \(p_{\mathrm {T}}^\mathrm {truth}\) and b in the lowest bin of track jet transverse momentum \(p_{\mathrm {T}}^{\mathrm {track\ jet}}\) considered in data

6.3 Derivation of pile-up correction parameters

Figure 6a and b shows the dependence of \(p_{\mathrm {T,\mathrm{EM}}}^{\mathrm {jet}}\), and thus \(\mathcal {O}^{\mathrm{EM}}\), on \(N_{\mathrm{PV}}\). In this example, narrow (\(R = 0.4\), \(\left| \eta _\mathrm{det}\right| < 2.1\)) and wide (\(R = 0.6\), \(\left| \eta _\mathrm{det}\right| < 1.9\)) central jets reconstructed in MC simulation are shown for events within a given range \(7.5 \le \mu < 8.5\). The jet \(p_{\mathrm {T}}\) varies by \(0.277 \pm 0.005\) GeV(in data) and \(0.288 \pm 0.003\) GeV(in MC simulations) per primary vertex for jets with \(R=0.4\) and by \(0.578 \pm 0.005\) GeV(in data) and \(0.601 \pm 0.003\) GeV(in MC simulations) per primary vertex for jets with \(R=0.6\). The slopes \(\alpha ^{\mathrm{EM}}\) are found to be independent of the true jet transverse momentum \(p_{\mathrm {T}}^\mathrm {truth}\), as expected from the diffuse character of in-time pile-up signal contributions.

A qualitatively similar behaviour can be observed in collision data for calorimeter jets individually matched with track jets, the latter reconstructed as discussed in Sect. 5.4. The \(N_{\mathrm{PV}}\) dependence of \(p_{\mathrm {T,\mathrm{EM}}}^{\mathrm {jet}}\) can be measured in bins of the track-jet transverse momentum \(p_{\mathrm {T}}^{\mathrm {track\ jet}}\). Jets formed from tracks are much less sensitive to pile-up and can be used as a stable reference to investigate pile-up effects. Figure 6c and d shows the results for the same calorimeter regions and out-of-time pile-up condition as for the MC-simulated jets in Fig. 6a, b. The results shown in Fig. 6 also confirm the expectation that the contributions from in-time pile-up to the jet signal are larger for wider jets (\(\alpha ^{\mathrm{EM}}(R = 0.6) > \alpha ^{\mathrm{EM}}(R = 0.4)\)), but scale only approximately with the size of the jet catchment area [78] determined by the choice of distance parameter \(R\) in the anti-\(k_{t}\) algorithm.
Fig. 8

The pile-up contribution per additional vertex, measured as \(\alpha ^{\mathrm{EM}}= \partial p_\mathrm{T, \mathrm{EM}}^\mathrm {jet}/\partial N_{\mathrm{PV}}\), as function of \(|\eta _\mathrm{det}|\), for the various methods discussed in the text, for a\(R = 0.4\) and b\(R = 0.6\) jets. The contribution from \(\mu \), calculated as \(\beta ^{\mathrm{EM}}= \partial p_\mathrm{T, \mathrm{EM}}^\mathrm {jet}/\partial \mu \) and displayed for the various methods as function of \(|\eta _\mathrm{det}|\), is shown for the two jet sizes in c and d, respectively. The points for the determination of \(\alpha ^{\mathrm{EM}}\) and \(\beta ^{\mathrm{EM}}\) from MC simulations use the offset calculated from the reconstructed \(p_{\mathrm {T,\mathrm{EM}}}^{\mathrm {jet}}\) and the true (particle level) \(p_{\mathrm {T}}^\mathrm {truth}\), as indicated in Eq. 4

The dependence of \(p_\mathrm{T, \mathrm{EM}}^\mathrm {jet}\) on \(\mu \), for a fixed \(N_{\mathrm{PV}}= 6\), is shown in Fig. 7a for MC simulations using truth jets, and in Fig. 7b for collision data using track jets. The kinematic bins shown are the lowest bins considered, with \(20 < p_{\mathrm {T}}^\mathrm {truth}< 25\) GeV and \(20 < p_{\mathrm {T}}^{\mathrm {track\ jet}}< 25\) GeV for MC simulations and data, respectively. The jet \(p_{\mathrm {T}}\) varies by \(0.047 \pm 0.003\) GeV (in MC simulations) \(0.105 \pm 0.003\) GeV (in data) per primary vertex for jets with \(R=0.4\).

The result confirms the expectations that the dependence of \(p_\mathrm{T, \mathrm{EM}}^\mathrm {jet}\) on the out-of-time pile-up is linear and significantly less than its dependence on the in-time pile-up contribution scaling with \(N_{\mathrm{PV}}\). Its magnitude is still different for jets with \(R = 0.6\), as the size of the jet catchment area again determines the absolute contribution to \(p_\mathrm{T, \mathrm{EM}}^\mathrm {jet}\).

The correction coefficients for jets calibrated with the EM+JES scheme, \(\alpha ^{\mathrm{EM}}\) and \(\beta ^{\mathrm{EM}}\), are both determined from MC simulations as functions of the jet direction \(\eta _\mathrm{det}\). For this, the \(N_{\mathrm{PV}}\) dependence of \(p_{\mathrm {T,\mathrm{EM}}}^{\mathrm {jet}}\)(\(\eta _\mathrm{det}\)) reconstructed in various bins of \(\mu \) in the simulation is fitted and then averaged, yielding \(\alpha ^{\mathrm{EM}}(\eta _\mathrm{det})\). Accordingly and independently, the dependence of \(p_{\mathrm {T,\mathrm{EM}}}^{\mathrm {jet}}\) on \(\mu \) is fitted in bins of \(N_{\mathrm{PV}}\), yielding the average \(\beta ^{\mathrm{EM}}(\eta _\mathrm{det})\), again using MC simulations. An identical procedure is used to find the correction functions \(\alpha ^{\mathrm{LCW}}(\eta _\mathrm{det})\) and \(\beta ^{\mathrm{LCW}}(\eta _\mathrm{det})\) for jets calibrated with the LCW+JES scheme.

The parameters \(\alpha ^{\mathrm{EM}}\)(\(\alpha ^{\mathrm{LCW}}\)) and \(\beta ^{\mathrm{EM}}\)(\(\beta ^{\mathrm{LCW}}\)) can be also measured with in situ techniques. This is discussed in Sect. 6.4.

6.4 Pile-up validation with in situ techniques and effect of out-of-time pile-up in different calorimeter regions

The parameters \(\alpha ^{\mathrm{EM}}\)(\(\alpha ^{\mathrm{LCW}}\)) and \(\beta ^{\mathrm{EM}}\)(\(\beta ^{\mathrm{LCW}}\)) can be measured in data with respect to a reference that is stable under pile-up using track jets or photons in \(\gamma \text {-jet}\) events as kinematic reference that does not depend on pile-up.

The variation of the \(p_{\mathrm {T}}\) balance \(p_{\mathrm {T,\mathrm{EM}}}^{\mathrm {jet}} - p_{\mathrm {T}}^{\gamma }\) (\(p_{\mathrm {T,\mathrm{LCW}}}^{\mathrm {jet}} - p_{\mathrm {T}}^{\gamma }\)) in \(\gamma \text {-jet}\) events can be used in data and MC simulation (similarly to the strategy discussed in Sect. 10), as a function of \(N_{\mathrm{PV}}\) and \(\mu \). Figure 8 summarises \(\alpha ^{\mathrm{EM}}(\eta _\mathrm{det})\) and \(\beta ^{\mathrm{EM}}(\eta _\mathrm{det})\) determined with track jets and \(\gamma \text {-jet}\) events, and their dependence on \(\eta _\mathrm{det}\). Both methods suffer from lack of statistics or large systematic uncertainties in the 2011 data, but are used in data-to-MC comparisons to determine systematic uncertainties of the MC-based method (see the corresponding discussion in Sect. 16.2).

The decrease of \(\beta ^{\mathrm{EM}}(\eta _\mathrm{det})\) towards higher \(\eta _\mathrm{det}\), as shown in Fig. 8c and d, indicates a decreasing signal contribution to \(p_{\mathrm {T,\mathrm{EM}}}^{\mathrm {jet}}\) per out-of-time pile-up interaction. For jets with \(\left| \eta _\mathrm{det}\right| > 1.5\), the offset is increasingly suppressed in the signal with increasing \(\mu \) (\(\beta ^{\mathrm{EM}}(\eta _\mathrm{det})< 0\)). This constitutes a qualitative departure from the behaviour of the pile-up history contribution in the central region of ATLAS, where this out-of-time pile-up leads to systematically increasing signal contributions with increasing \(\mu \).

This is a consequence of two effects. First, for \(\left| \eta _\mathrm{det}\right| \) larger than about 1.7 the hadronic calorimetry in ATLAS changes from the Tile calorimeter to the LAr end-cap (HEC) calorimeter. The Tile calorimeter has a unipolar and fast signal shape [20]. It has little sensitivity to out-of-time pile-up, with an approximate shape signal baseline of 150 ns. The out-of-time history manifests itself in this calorimeter as a small positive increase of its contribution to the jet signal with increasing \(\mu \).

The HEC, on the other hand, has the typical ATLAS LAr calorimeter bipolar pulse shape with approximately 600 ns baseline. This leads to an increasing suppression of the contribution from this calorimeter to the jet signal with increasing \(\mu \), as more activity from the pile-up history increases the contribution weighted by the negative pulse shape.

Second, for \(\left| \eta _\mathrm{det}\right| \) larger than approximately 3.2, coverage is provided by the ATLAS forward calorimeter (FCal). While still a liquid-argon calorimeter, the FCal features a considerably faster signal due to very thin argon gaps. The shaping function for this signal is bipolar with a net zero integral and a similar positive shape as in other ATLAS liquid-argon calorimeters, but with a shorter overall pulse baseline (approximately 400 ns). Thus, the FCal shaping function has larger negative weights for out-of-time pile-up of up to 70 % of the (positive) pulse peak height, as compared to typically 10–20 % in the other LAr calorimeters [19]. These larger negative weights lead to larger signal suppression with increasing activity in the pile-up history and thus with increasing \(\mu \).

7 In situ transverse momentum balance techniques

In this section an overview is given on how the data-to-MC differences are assessed using in situ techniques exploiting the transverse momentum balance between the jet and a well-measured reference object.

The calibration of jets in the forward region of the detector relative to jets in the central regions is discussed in more detail in Sect. 8. Jets in the central region are calibrated using photons or \(Z\) bosons as reference objects up to a transverse momentum of 800 GeV (see Sects. 9 and 10). Jets with higher \(p_{\mathrm {T}}\) are calibrated using a system of low-\(p_{\mathrm {T}}\) jets recoiling against a high-\(p_{\mathrm {T}}\) jet (see Sect. 11).

7.1 Relative in situ calibration between the central and forward rapidity regions

Transverse momentum balance in dijet events is exploited to study the pseudorapidity dependence of the jet response. A relative \(\eta \)-intercalibration is derived using the matrix method described in Ref. [3] to correct the jets in data for residual effects not captured by the initial calibration derived from MC simulations and based on truth jets. This method is applied for jets with \(20 \le p_{\mathrm {T}}^\mathrm {jet}<1500\) GeV and \(|\eta _\mathrm{det}| \le 4.5\). Jets up to \(|\eta _\mathrm{det}| = 2.8\) are calibrated using \(|\eta _\mathrm{det}| < 0.8\) as a reference region. For jets with \(\eta _\mathrm{det}>2.8\) (\(\eta _\mathrm{det}<-2.8\)), for which the uncertainty on the derived calibration becomes large, the calibration determined at \(\eta _\mathrm{det}=2.8\) (\(\eta _\mathrm{det}=-2.8\)) is used.6 Jets that fall in the reference region receive no additional correction on average. The \(\eta \)-intercalibration is applied to all jets prior to deriving the absolute calibration of the central region.

The largest uncertainty of the dijet balance technique is due to the modelling of the additional parton radiation altering the \(p_{\mathrm {T}}\) balance. This uncertainty is estimated using MC simulations employing the Pythia and Herwig++ generators, respectively.

7.2 In situ calibration methods for the central rapidity region

The energy scale of jets is tested in situ using a well-calibrated object as reference. The following techniques are used for the central rapidity region \(\eta _\mathrm{det}< 1.2\):
  1. 1.

    Direct transverse momentum balance between a photon or a\(\varvec{Z}\)boson and a jet Events with a photon or a \(Z\) boson and a recoiling jet are used to directly compare the transverse momentum of the jet to that of the photon or the \(Z\) boson (direct balance, DB). The data are compared to MC simulations in the jet pseudorapidity range \(|\eta _\mathrm{det}| < 1.2\). The \(\gamma \text {-jet}\) analysis covers a range in photon transverse momentum from 25 to 800 GeV, while the \(Z\)-jet analysis covers a range in \(Z\) transverse momentum from 15 to 200 GeV. However, only the direct transverse momentum balance between the \(Z\) and the jet is used in the derivation of the residual JES correction, as the method employing \(p_{\mathrm {T}}\) balance between a photon and the full hadronic recoil, rather than the jet (see item 2 below), is used in place of the direct \(\gamma \text {-jet}\) balance, see Sect. 13.5 for more details.

     
  2. 2.

    Transverse momentum balance between a photon and the hadronic recoil The photon transverse momentum is balanced against the full hadronic recoil using the projection of the missing transverse momentum onto the photon direction. With this missing transverse momentum projection fraction (MPF) technique, the calorimeter response for the hadronic recoil is measured, which is independent of any jet definition. The comparison is done in the same kinematic region as the direct photon balance method.

     
  3. 3.

    Balance between a low-\(\varvec{p}_{{\mathbf {T}}}\)jet system and a high-\(\varvec{p}_{{\mathbf {T}}}\)jet Jets at high \(p_{\mathrm {T}}\) can be balanced against a recoil system of low \(p_{\mathrm {T}}\) jets within \(\eta _\mathrm{det}< 2.8\) if the low \(p_{\mathrm {T}}\) jets are well calibrated using \(\gamma \text {-jet}\) or \(Z\)-jet in situ techniques. The multijet balance can be iterated several times to increase the non-leading (in terms of \(p_{\mathrm {T}}\)) jets \(p_{\mathrm {T}}\) range beyond the values covered by \(\gamma \text {-jet}\) or \(Z\)-jet balance, and reaching higher \(p_{\mathrm {T}}\) of the leading jet, until statistical limitations preclude a precise measurement. This method can probe the jet energy scale up to the  TeV regime.

     
In addition to the methods mentioned above, the mean transverse momentum sum of tracks within a cone around the jet direction provides an independent test of the calorimeter energy scale over the entire measured \(p_{\mathrm {T}}^\mathrm {jet}\) range within the tracking acceptance. This method, described in Ref. [3], is used for the 2010 dataset and is also studied for the inclusive jet data sample in 2011. It is also used for b-jets (see Sect. 19). However, because of the relatively large associated systematic uncertainties, it is not included in the JES calibration derived from the combination of in situ methods for inclusive jets in 2011. This calibration can be constrained to much higher quality by applying the three methods described above.

8 Relative forward-jet calibration using dijet events

The calibration of the forward detector can be performed by exploiting the transverse momentum balance in events with two jets at high transverse momentum. A well calibrated jet in the central part of the detector is balanced against a jet in the forward region.

Thus the whole detector acceptance in \(\eta \) can be equalised as a function of \(p_{\mathrm {T}}^\mathrm {jet}\). In addition to this simple approach, a matrix method is used where jets in all regions (and not only the central one) are used for the \(\eta \)-intercalibration.

In the following the results for the EM+JES scheme are discussed as an example. While the measured relative response can deviate by a few percent between the EM+JES and the LCW+JES calibration schemes, the ratio between data and Monte Carlo simulation agrees within a few permille.

8.1 Intercalibration using events with dijet topologies

8.1.1 Intercalibration using a central reference region

The standard approach for \(\eta \)-intercalibration with dijet events is to use the central region of the calorimeters as the reference region, as described in Ref. [79]. The relative calorimeter response of jets in other calorimeter regions is measured by the \(p_{\mathrm {T}}\) balance between the reference jet (with \(p_{\mathrm {T}}^{\mathrm {ref}}\)) and the probe jet (with \(p_{\mathrm {T}}^{\mathrm {probe}}\)), exploiting the fact that these jets are expected to have equal \(p_{\mathrm {T}}\) due to transverse momentum conservation. The \(p_{\mathrm {T}}\) balance is expressed in terms of the asymmetry \(\mathcal {A}\),
$$\begin{aligned} \mathcal {A}= \frac{p_{\mathrm {T}}^{\mathrm {probe}}- p_{\mathrm {T}}^{\mathrm {ref}}}{p_{\mathrm {T}}^\mathrm {avg}}, \end{aligned}$$
(7)
with \(p_{\mathrm {T}}^\mathrm {avg}= (p_{\mathrm {T}}^{\mathrm {probe}}+ p_{\mathrm {T}}^{\mathrm {ref}})/2\). The reference region is chosen as the central region of the barrel calorimeter, given by \(|\eta _\mathrm{det}|<0.8\). If both jets fall into the reference region, each jet is used, in turn, to probe the other. As a consequence, the average asymmetry in the reference region will be zero by construction.
The asymmetry is then used to measure an \(\eta \)-intercalibration factor \(c\) of the probe jet, or its response relative to the reference jet \(1/c\), using the relation
$$\begin{aligned} \frac{p_{\mathrm {T}}^{\mathrm {probe}}}{p_{\mathrm {T}}^{\mathrm {ref}}} =\frac{2+\mathcal {A}}{2-\mathcal {A}} =1/c. \end{aligned}$$
(8)
The measurement of \(c\) is performed in bins of jet \(\eta _\mathrm{det}\) and \(p_{\mathrm {T}}^\mathrm {avg}\), where \(\eta _\mathrm{det}\) is defined as discussed in Sect. 5.6. Using the standard method outlined above, there is an asymmetry distribution \(\mathcal {A}_{ik}\) for each probe jet \(\eta _\mathrm{det}\) bin \(i\) and each \(p_{\mathrm {T}}^\mathrm {avg}\) bin \(k\) An overview of the binning is given in Fig. 9 for jets with \(R=0.4\) calibrated with the EM+JES scheme. The same bins are used for jets calibrated with the EM+JES or LCW+JES scheme. However, the binning is changed for jets with \(R=0.6\) to take the different trigger thresholds into account. Intercalibration factors are calculated for each bin according to Eq. (8), resulting in
$$\begin{aligned} c_{ik}=\frac{2-\langle \mathcal {A}_{ik}\rangle }{2+\langle \mathcal {A}_{ik}\rangle }, \end{aligned}$$
where the \(\langle \mathcal {A}_{ik}\rangle \) is the mean value of the asymmetry distribution in each bin. The uncertainty on \(\langle \mathcal {A}_{ik}\rangle \) is taken to be the RMS/\(\sqrt{N}\) of each distribution. For the data, \(N\) is the number of events in the bin, while for the MC sample the number of effective events \(N_{\mathrm {eff}}\) is used (\(N = N_{\mathrm {eff}}\)) to incorporate MC event weights \(w_{k}\),
$$\begin{aligned} N_{\mathrm {eff}} = \left( \sum {w_k}\right) ^2/\sum {w_k^2}. \end{aligned}$$
Here the sums are running over all events of the MC sample. The above procedure is referred to as the central reference method.
Fig. 9

Overview of the \((p_{\mathrm {T}}^\mathrm {avg},\eta _\mathrm{det})\) bins of the dijet balance measurements for jets reconstructed with distance parameter \(R=0.4\) calibrated using the EM+JES scheme. The solid lines indicate the \(( p_{\mathrm {T}}^\mathrm {avg},\eta _{\mathrm {det}}^{\mathrm {probe}})\) bin edges, and the points show the average transverse momentum and pseudorapidity of the probe jet within each bin. The measurements within the \(\eta _\mathrm{det}\) range spanned by the two thick, dashed lines are used to derive the residual calibration

8.1.2 Intercalibration using the matrix method

A disadvantage with the central reference method outlined above is that all events are required to have a jet in the central reference region. This results in a significant loss of event statistics, especially in the forward region, where the dijet cross section drops steeply as the rapidity interval between the jets increases. In order to use the full statistics, one can extend the central reference method by replacing the probe and reference jets by “left” and “right” jets, defined by \(\eta _{\mathrm {det}}^{\mathrm {left}}<\eta _{\mathrm {det}}^{\mathrm {right}}\). Equations (7) and (8) then become
$$\begin{aligned} \mathcal {A}\!=\! \frac{p_{\mathrm {T}}^\mathrm{left}\! -\! p_{\mathrm {T}}^\mathrm{right}}{p_{\mathrm {T}}^\mathrm {avg}}, \qquad \mathrm {and} \qquad \mathcal {R}= \frac{p_{\mathrm {T}}^\mathrm{left}}{p_{\mathrm {T}}^\mathrm{right}}\!=\! \frac{c^\mathrm{right}}{c^\mathrm{left}}\!=\! \frac{2+\mathcal {A}}{2-\mathcal {A}}, \end{aligned}$$
where the term \(\mathcal {R}\) denotes the ratio of the responses, and \(c^\mathrm{left}\) and \(c^\mathrm{right}\) are the \(\eta \)-intercalibration factors for the left and right jet, respectively.
This approach yields response ratio (\(\mathcal {R}_{ijk}\)) distributions with an average value \(\langle R_{ijk}\rangle \), evaluated for each \(\eta _{\mathrm {det}}^{\mathrm {left}}\) bin \(i\), \(\eta _{\mathrm {det}}^{\mathrm {right}}\) bin \(j\), and \(p_{\mathrm {T}}^\mathrm {avg}\) bin \(k\). The relative correction factor \(c_{ik}\) for a given jet in \(\eta _\mathrm{det}\) bin \(i\), with \(i = 1 \ldots N\), and for a fixed \(p_{\mathrm {T}}^\mathrm {avg}\) bin \(k\) is then obtained by a minimisation procedure using a set of \(N\) equations,
$$\begin{aligned} S(c_{1k},\ldots ,c_{Nk})&= \sum _{j=1}^{N} \sum _{i=1}^{j-1} \left( \frac{1}{\Delta \langle \mathcal {R}_{ijk} \rangle }\left( c_{ik} \langle \mathcal {R}_{ijk} \rangle -c_{jk}\right) \right) ^2\nonumber \\&\quad {} +X(c_{1k},\ldots ,c_{Nk}) . \end{aligned}$$
(9)
Here \(\Delta \langle \mathcal {R}\rangle \) is the statistical uncertainty of \(\langle \mathcal {R}\rangle \) and the function \(X(c_{ik})\) is used to quadratically suppress deviations from unity of the average corrections,7
$$\begin{aligned} X(c_{1k},\ldots ,c_{Nk})=K\left( N^{-1} \sum _{i=1}^{N} c_{ik} - 1\right) ^{2}. \end{aligned}$$
The value of the constant \(K\) does not influence the solution as long as it is sufficiently large, e.g. \(K \approx N_{\mathrm {bins}}\), where \(N_{\mathrm {bins}}\) is the number of \(\eta _\mathrm{det}\) bins. The minimisation according to Eq. (9) is performed separately for each \(p_{\mathrm {T}}\) bin \(k\), and the resulting calibration factors \(c_{i}\) obtained in each \(\eta _\mathrm{det}\) bin \(i\) are scaled such that the average calibration factor in the reference region \(|\eta _\mathrm{det}|<0.8\) equals unity. This method is referred to as the matrix method.

8.2 Event selection for dijet analysis

8.2.1 Trigger selection

Events are retained from the calorimeter trigger stream using a combination of central (\(|\eta _\mathrm{det}|<3.2\)) and forward (\(|\eta _\mathrm{det}|>3.1\)) jet triggers [18].

The selection is designed such that the trigger efficiency for a specific region of \(p_{\mathrm {T}}^\mathrm {avg}{}\) is greater than 99 %, and approximately flat as a function of the pseudorapidity of the probe jet. Due to the different prescales for the central and forward jet triggers, the data collected by each trigger correspond to different integrated luminosities. To correctly normalise the data, events are assigned weights depending on the luminosity and the trigger decisions, according to the exclusion method described in Ref. [80].
Table 1

Summary of the event topology selection criteria applied in this analysis. The symbols “\(\text {jet}1\)” and “\(\text {jet}2\)” refer to the leading two jets (two highest-\(p_{\mathrm {T}}\) jets), while “\(\text {jet}3\)” indicates the highest-\(p_{\mathrm {T}}\) sub-leading (third) jet in the event

Variable

Selection

\(\Delta \phi (\text {jet}1,\text {jet}2)\)

\(>\)2.5 rad

\(p_{\text {T}}^{\,\text {jet}3}\), \(|\eta _\mathrm{det}(\text {jet}3)| < 2.5\)

\(<\)\(\max (0.25\, p_{\mathrm {T}}^\mathrm {avg}{}, 12 ~\mathrm{GeV })\)

\(p_{\text {T}}^{\,\text {jet}3}\), \(|\eta _\mathrm{det}(\text {jet}3)| > 2.5\)

\(<\)\(\max (0.20\, p_{\mathrm {T}}^\mathrm {avg}{}, 10 ~\mathrm{GeV })\)

\(\mathrm {JVF}(\text {jet}3)\), \(|\eta _\mathrm{det}(\text {jet}3)|<2.5\)

\(>\)0.6

8.2.2 Dataset and jet quality selection

All ATLAS sub-detectors are required to be operational and events are rejected if any data-quality issues are present. The leading two jets are required to fulfil the default set of jet quality criteria (see Sect. 5.3). A dead calorimeter region was present for a subset of the data. To remove any bias from this region, events are removed if any jets are reconstructed close to this region.
Fig. 10

Relative jet response (\(1/c\)) for anti-\(k_{t}\) jets with \(R=0.4\) calibrated with the EM+JES scheme as a function of the probe jet pseudorapidity measured using the matrix and the central reference methods. Results are presented in a for \(40 \le p_{\mathrm {T}}^\mathrm {avg}<55\) GeV and in b for \(220 \le p_{\mathrm {T}}^\mathrm {avg}<300\) GeV. The lower parts of the figures show the ratios between relative response in data and MC

8.2.3 Dijet topology selection

In order to use the momentum balance of dijet events to measure the jet response, it is important that the events used have a relatively clean \(2 \rightarrow 2\) topology. If a third jet is produced in the same hard-scatter proton–proton interaction, the balance between the leading (in \(p_{\mathrm {T}}\)) two jets is affected. To enhance the number of events in the sample that have this \(2 \rightarrow 2\) topology, selection criteria on the azimuthal angle \(\Delta \phi (\text {jet}1,\text {jet}2)\) between the two leading jets, and \(p_{\mathrm {T}}\) requirements on additional jets are applied. Table 1 summarises these topology selection criteria.

In addition, all jets used for balancing and topology selection have to originate from the hard-scattering vertex, and not from a vertex reconstructed from a pile-up interaction. For this, each jet considered is evaluated with respect to its jet vertex fraction (\(\mathrm {JVF}\)), a likelihood measure estimating the vertex contribution to a jet [3]. To calculate \(\mathrm {JVF}\), reconstructed tracks originating from reconstructed primary vertices \(i = 1,\ldots ,N_{\mathrm{PV}}\) are matched to jets using an angular matching criterion in \((\eta ,\phi )\) space of \(\Delta R < 0.4\) with respect to the jet axis. The track parameters calculated at the distance of closest approach to the selected hard-scattering vertex are used for this matching. For each jet, the scalar sum of the \(p_{\mathrm {T}}\) of these matched tracks, \(\Sigma _i\), is calculated for each vertex \(i\) contributing to the jet. The \(\mathrm {JVF}\) variable is then defined as the \(p_{\mathrm {T}}\) sum for the hard-scattering vertex, \(\Sigma _0\), divided by the sum of \(\Sigma _i\) over all primary vertices. Any jet that has \(|\eta _\mathrm{det}|<2.5\) and \(\mathrm {JVF}>0.6\) is classified as “vertex confirmed” since it is likely to originate from the hard-scattering vertex.

This selection differs from that used in previous studies [3] due to the much higher instantaneous luminosities experienced during data taking and the consequentially increasing pile-up. In the forward region \(|\eta _\mathrm{det}|>2.5\), no tracking is available, and events containing any additional forward jet with significant \(p_{\mathrm {T}}\) are removed (see the third criteria in Table 1).

8.3 Dijet balance results

8.3.1 Binning of the balance measurements

An overview of the (\(p_{\mathrm {T}}^\mathrm {avg}\),\(\eta _\mathrm{det}\)) bins used in the analysis is presented in Fig. 9. All events falling in a given \(p_{\mathrm {T}}^\mathrm {avg}\) bin are collected using a dedicated central and forward trigger combination. The statistics in each \(p_{\mathrm {T}}^\mathrm {avg}\) bin are similar, except for the highest and lowest bins which contain fewer events. The loss of statistical precision of the measurements for the lower \(p_{\mathrm {T}}^\mathrm {avg}\) bins is introduced by a larger sensitivity to the inefficiency of the pile-up suppression strategy, which rejects relatively more events due to the kinematic overlap of the hard-scatter jets with jets from pile-up. In addition, the asymmetry distribution broadens due to worsening relative jet \(p_{\mathrm {T}}\) resolution, leading to larger fluctuations in this observable.

Each \(p_{\mathrm {T}}^\mathrm {avg}\) bin is further divided into several \(\eta _\mathrm{det}\) bins. The \(\eta _\mathrm{det}\) binning is motivated by detector geometry and statistics.
Fig. 11

Relative jet response (\(1/c\)) as a function of the jet pseudorapidity for anti-\(k_{t}\) jets with \(R=0.4\) calibrated with the EM+JES scheme, separately for a\(22 \le p_{\mathrm {T}}^\mathrm {avg}{}<30\) GeV, b\(55 \le p_{\mathrm {T}}^\mathrm {avg}{}<75\) GeV, c\(170 \le p_{\mathrm {T}}^\mathrm {avg}{}<220\) GeV and d\(600 \le p_{\mathrm {T}}^\mathrm {avg}{}<800\) GeV. The lower parts of the figures show the ratios between the data and MC relative response. These measurements are performed using the matrix method. The applied correction is shown as a thick line. The line is solid over the range where the measurements is used to constrain the calibration, and dashed in the range where extrapolation is applied

Fig. 12

Relative jet response (\(1/c\)) as a function of the average jet \(p_{\mathrm {T}}\) of the dijet system for anti-\(k_{t}\) jets with \(R=0.4\) calibrated with the EM+JES scheme, separately for a\(-1.2\le \eta _\mathrm{det}<-0.8\) and b\(2.1\le \eta _\mathrm{det}<2.8\). The lower parts of the figures show the ratios between the data and MC relative response. The applied correction is shown as a thick line

8.3.2 Comparison of intercalibration methods

The relative jet response obtained with the matrix method is compared to the relative jet response obtained using the central reference method. Figure 10a and b show the jet response relative to central jets (\(1/c\)) for two \(p_{\mathrm {T}}^\mathrm {avg}\) bins, \(40 \le p_{\mathrm {T}}^\mathrm {avg}{}<55\) GeV and \(220\le p_{\mathrm {T}}^\mathrm {avg}{}<300\) GeV. In the most forward region at low \(p_{\mathrm {T}}\), the matrix method tends to give a slightly higher relative response compared to the central reference method (see Fig. 10a). However, the same relative shift is observed both for data and MC simulations, and consequently the data-to-MC ratios are consistent. The matrix method is therefore used to measure the relative response as it has better statistical precision.

8.3.3 Comparison of data with Monte Carlo simulation

Figure 11 shows the relative response obtained using the matrix method as a function of the jet pseudorapidity for data and MC simulations. Four different \(p_{\mathrm {T}}^\mathrm {avg}\) regions are shown, \(22\le p_{\mathrm {T}}^\mathrm {avg}<30\) GeV, \(55\le p_{\mathrm {T}}^\mathrm {avg}<75\) GeV, \(170\le p_{\mathrm {T}}^\mathrm {avg}<220\) GeV, and \(600\le p_{\mathrm {T}}^\mathrm {avg}<800\) GeV. Figure 12 shows the relative response as a function of \(p_{\mathrm {T}}^\mathrm {avg}\) for two representative \(\eta _\mathrm{det}\) bins, namely \(-1.2\le \eta _\mathrm{det}<-0.8\) and \(2.1\le \eta _\mathrm{det}<2.8\). The general features of the response in data are reasonably well reproduced by the MC simulations. However, as observed in previous studies [3], the Herwig++ MC generator predicts a higher relative response than Pythia for jets outside the central reference region (\(|\eta _\mathrm{det}|>0.8\)). Data tend to fall in-between the two predictions. This discrepancy was investigated and is observed both for truth jets built from stable particles (before any detector modelling), and also jets built from partons (before hadronisation). The differences therefore reflect a difference in physics modelling between the event generators, most likely due to the parton showering. The Pythia predictions are based upon a \(p_{\mathrm {T}}\)-ordered parton shower whereas the Herwig++ predictions are based on an angular-ordered parton shower.

For \(p_{\mathrm {T}}{}>40\) GeV and \(|\eta _\mathrm{det}|<2\), Pythia tends to agree better with data than Herwig++ does. In the more forward region, the spread between the Pythia and Herwig++ response predictions increases and reaches approximately 5 % at \(|\eta _\mathrm{det}|=4\). In the more forward region (\(|\eta _\mathrm{det}|>3\)) the relative response prediction of Herwig++ generally agrees better with data than Pythia.

8.3.4 Derivation of the residual correction

The residual calibration is derived from the data/Pythia ratio \(C_i = c^\mathrm{data}_i/c^{\textsc {Pythia}}_i\) of the measured \(\eta \)-intercalibration factors. Pythia is used as the reference as it is also used to obtain the initial (main) calibration, see Sect. 5. The correction is a function of jet \(p_{\mathrm {T}}\) and \(\eta _\mathrm{det}\) (\(F_\mathrm{rel}(p_{\mathrm {T}},\eta _\mathrm{det})\)) and is constructed by combining the \(N_\mathrm{bins}\) measurements of the \((p_{\mathrm {T}}^\mathrm {avg},\eta _\mathrm{det})\) bins using a two-dimensional Gaussian kernel, like
$$\begin{aligned} F_\mathrm{rel}(p_{\mathrm {T}},\eta _\mathrm{det}) = \dfrac{\sum _{i=1}^{N_\mathrm{bins}} C_i w_i}{\sum _{i=1}^{N_\mathrm{bins}} w_i}, \end{aligned}$$
with
$$\begin{aligned} w_i \!=\! \dfrac{1}{\Delta C_i^2} \!\times \! \mathrm{Gaus}\left( \dfrac{\log p_{\mathrm {T}}\!-\! \log \langle p_{\mathrm {T}}^{\mathrm {probe}}\rangle _i}{\sigma _{\log p_{\mathrm {T}}}} \oplus \dfrac{\eta _\mathrm{det}\!-\! \langle \eta _\mathrm{det}\rangle _i}{\sigma _\eta }\!\right) \!. \end{aligned}$$
Here \(i\) denotes the index of a \((p_{\mathrm {T}}^\mathrm {avg},\eta _\mathrm{det})\)-bin, \(\Delta C_i\) is the statistical uncertainty of \(C_i\),\(\langle p_{\mathrm {T}}^{\mathrm {probe}}\rangle _i\) and \(\langle \eta _\mathrm{det}\rangle _i\) are the average \(p_{\mathrm {T}}\) and \(\eta _\mathrm{det}\) of the probe jets in the bin (see Fig. 9). The Gaussian function has a central value of zero and a width controlled by \(\sigma _{\log p_{\mathrm {T}}}\) and \(\sigma _\eta \).

Only the measurements with \(|\eta _\mathrm{det}|<2.8\) are included in the derivation of the correction function because of the large discrepancy between the modelled response of the MC simulation samples in the more forward region. This \(\eta _\mathrm{det}\) boundary is indicated by a thick, dashed line in Fig. 9. The residual correction is held fixed for pseudorapidities larger than those of the most forward measurements included (\(|\eta _\mathrm{det}|\approx 2.4\)). All jets with a given \(p_{\mathrm {T}}\) and \(|\eta _\mathrm{det}|>2.4\) will hence receive the same \(\eta \)-intercalibration correction. The kernel-width parameters used8 are found to capture the shape of the data-to-MC ratio, but at the same time provide stability against statistical fluctuations. This choice introduces a stronger constraint across \(p_{\mathrm {T}}\). The resulting residual correction is shown as a thick line in the lower sections of Figs. 11 and 12. The line is solid over the range where the measurements is used to constrain the calibration, and dashed in the range where extrapolation is applied.

8.4 Systematic uncertainty

The observed difference in the relative response between data and MC simulations could be due to mis-modelling of physics or detector effects used in the simulation. Suppression and selection criteria used in the analysis (e.g. topol