Jet energy measurement with the ATLAS detector in proton-proton collisions at \(\sqrt{\mathrm{s}}=7\ \mathrm{TeV}\)

  • The ATLAS Collaboration
  • G. Aad
  • B. Abbott
  • J. Abdallah
  • A. A. Abdelalim
  • A. Abdesselam
  • O. Abdinov
  • B. Abi
  • M. Abolins
  • H. Abramowicz
  • H. Abreu
  • E. Acerbi
  • B. S. Acharya
  • D. L. Adams
  • T. N. Addy
  • J. Adelman
  • M. Aderholz
  • S. Adomeit
  • P. Adragna
  • T. Adye
  • S. Aefsky
  • J. A. Aguilar-Saavedra
  • M. Aharrouche
  • S. P. Ahlen
  • F. Ahles
  • A. Ahmad
  • M. Ahsan
  • G. Aielli
  • T. Akdogan
  • T. P. A. Åkesson
  • G. Akimoto
  • A. V. Akimov
  • A. Akiyama
  • A. Aktas
  • M. S. Alam
  • M. A. Alam
  • J. Albert
  • S. Albrand
  • M. Aleksa
  • I. N. Aleksandrov
  • F. Alessandria
  • C. Alexa
  • G. Alexander
  • G. Alexandre
  • T. Alexopoulos
  • M. Alhroob
  • M. Aliev
  • G. Alimonti
  • J. Alison
  • M. Aliyev
  • P. P. Allport
  • S. E. Allwood-Spiers
  • J. Almond
  • A. Aloisio
  • R. Alon
  • A. Alonso
  • M. G. Alviggi
  • K. Amako
  • P. Amaral
  • C. Amelung
  • V. V. Ammosov
  • A. Amorim
  • G. Amorós
  • N. Amram
  • C. Anastopoulos
  • L. S. Ancu
  • N. Andari
  • T. Andeen
  • C. F. Anders
  • G. Anders
  • K. J. Anderson
  • A. Andreazza
  • V. Andrei
  • M-L. Andrieux
  • X. S. Anduaga
  • A. Angerami
  • F. Anghinolfi
  • N. Anjos
  • A. Annovi
  • A. Antonaki
  • M. Antonelli
  • A. Antonov
  • J. Antos
  • F. Anulli
  • S. Aoun
  • L. Aperio Bella
  • R. Apolle
  • G. Arabidze
  • I. Aracena
  • Y. Arai
  • A. T. H. Arce
  • J. P. Archambault
  • S. Arfaoui
  • J-F. Arguin
  • E. Arik
  • M. Arik
  • A. J. Armbruster
  • O. Arnaez
  • C. Arnault
  • A. Artamonov
  • G. Artoni
  • D. Arutinov
  • S. Asai
  • R. Asfandiyarov
  • S. Ask
  • B. Åsman
  • D. Asner
  • L. Asquith
  • K. Assamagan
  • A. Astbury
  • A. Astvatsatourov
  • G. Atoian
  • B. Aubert
  • E. Auge
  • K. Augsten
  • M. Aurousseau
  • N. Austin
  • G. Avolio
  • R. Avramidou
  • D. Axen
  • C. Ay
  • G. Azuelos
  • Y. Azuma
  • M. A. Baak
  • G. Baccaglioni
  • C. Bacci
  • A. M. Bach
  • H. Bachacou
  • K. Bachas
  • G. Bachy
  • M. Backes
  • M. Backhaus
  • E. Badescu
  • P. Bagnaia
  • S. Bahinipati
  • Y. Bai
  • D. C. Bailey
  • T. Bain
  • J. T. Baines
  • O. K. Baker
  • M. D. Baker
  • S. Baker
  • E. Banas
  • P. Banerjee
  • Sw. Banerjee
  • D. Banfi
  • A. Bangert
  • V. Bansal
  • H. S. Bansil
  • L. Barak
  • S. P. Baranov
  • A. Barashkou
  • A. Barbaro Galtieri
  • T. Barber
  • E. L. Barberio
  • D. Barberis
  • M. Barbero
  • D. Y. Bardin
  • 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
  • P. Barrillon
  • R. Bartoldus
  • A. E. Barton
  • D. Bartsch
  • V. Bartsch
  • R. L. Bates
  • L. Batkova
  • J. R. Batley
  • A. Battaglia
  • M. Battistin
  • G. Battistoni
  • F. Bauer
  • H. S. Bawa
  • B. Beare
  • T. Beau
  • P. H. Beauchemin
  • R. Beccherle
  • P. Bechtle
  • H. P. Beck
  • G. A. Beck
  • M. Beckingham
  • K. H. Becks
  • A. J. Beddall
  • A. Beddall
  • S. Bedikian
  • V. A. Bednyakov
  • C. P. Bee
  • M. Begel
  • S. Behar Harpaz
  • P. K. Behera
  • M. Beimforde
  • C. Belanger-Champagne
  • P. J. Bell
  • W. H. Bell
  • G. Bella
  • L. Bellagamba
  • F. Bellina
  • M. Bellomo
  • A. Belloni
  • O. Beloborodova
  • K. Belotskiy
  • O. Beltramello
  • S. Ben Ami
  • O. Benary
  • D. Benchekroun
  • C. Benchouk
  • M. Bendel
  • N. Benekos
  • Y. Benhammou
  • D. P. Benjamin
  • M. Benoit
  • J. R. Bensinger
  • K. Benslama
  • S. Bentvelsen
  • M. Beretta
  • D. Berge
  • E. Bergeaas Kuutmann
  • N. Berger
  • F. Berghaus
  • E. Berglund
  • J. Beringer
  • K. Bernardet
  • P. Bernat
  • R. Bernhard
  • C. Bernius
  • T. Berry
  • A. Bertin
  • F. Bertinelli
  • F. Bertolucci
  • M. I. Besana
  • N. Besson
  • S. Bethke
  • W. Bhimji
  • R. M. Bianchi
  • M. Bianco
  • O. Biebel
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  • J. Biesiada
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  • H. Bilokon
  • M. Bindi
  • S. Binet
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  • U. Bitenc
  • K. M. Black
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  • C. Blocker
  • J. Blocki
  • A. Blondel
  • W. Blum
  • U. Blumenschein
  • G. J. Bobbink
  • V. B. Bobrovnikov
  • S. S. Bocchetta
  • A. Bocci
  • C. R. Boddy
  • M. Boehler
  • J. Boek
  • N. Boelaert
  • S. Böser
  • J. A. Bogaerts
  • A. Bogdanchikov
  • A. Bogouch
  • C. Bohm
  • V. Boisvert
  • T. Bold
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  • N. M. Bolnet
  • M. Bona
  • V. G. Bondarenko
  • M. Bondioli
  • M. Boonekamp
  • G. Boorman
  • C. N. Booth
  • S. Bordoni
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  • A. Borisov
  • G. Borissov
  • I. Borjanovic
  • S. Borroni
  • K. Bos
  • D. Boscherini
  • M. Bosman
  • H. Boterenbrood
  • D. Botterill
  • J. Bouchami
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  • N. Bousson
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  • O. Brandt
  • U. Bratzler
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  • H. M. Braun
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  • G. Brown
  • H. Brown
  • P. A. Bruckman de Renstrom
  • D. Bruncko
  • R. Bruneliere
  • S. Brunet
  • A. Bruni
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  • M. Bruschi
  • T. Buanes
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  • J. Buchanan
  • N. J. Buchanan
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  • A. G. Buckley
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  • V. Büscher
  • L. Bugge
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  • O. Bulekov
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  • F. Butin
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  • J. M. Butler
  • C. M. Buttar
  • J. M. Butterworth
  • W. Buttinger
  • J. Caballero
  • S. Cabrera Urbán
  • D. Caforio
  • O. Cakir
  • P. Calafiura
  • G. Calderini
  • P. Calfayan
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  • L. P. Caloba
  • R. Caloi
  • D. Calvet
  • S. Calvet
  • R. Camacho Toro
  • P. Camarri
  • M. Cambiaghi
  • D. Cameron
  • S. Campana
  • M. Campanelli
  • V. Canale
  • F. Canelli
  • A. Canepa
  • J. Cantero
  • L. Capasso
  • M. D. M. Capeans Garrido
  • I. Caprini
  • M. Caprini
  • D. Capriotti
  • M. Capua
  • R. Caputo
  • C. Caramarcu
  • R. Cardarelli
  • T. Carli
  • G. Carlino
  • L. Carminati
  • B. Caron
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  • G. D. Carrillo Montoya
  • A. A. Carter
  • J. R. Carter
  • J. Carvalho
  • D. Casadei
  • M. P. Casado
  • M. Cascella
  • C. Caso
  • A. M. Castaneda Hernandez
  • E. Castaneda-Miranda
  • V. Castillo Gimenez
  • N. F. Castro
  • G. Cataldi
  • F. Cataneo
  • A. Catinaccio
  • J. R. Catmore
  • A. Cattai
  • G. Cattani
  • S. Caughron
  • D. Cauz
  • P. Cavalleri
  • D. Cavalli
  • M. Cavalli-Sforza
  • V. Cavasinni
  • F. Ceradini
  • A. S. Cerqueira
  • A. Cerri
  • L. Cerrito
  • F. Cerutti
  • S. A. Cetin
  • F. Cevenini
  • A. Chafaq
  • D. Chakraborty
  • K. Chan
  • B. Chapleau
  • J. D. Chapman
  • J. W. Chapman
  • E. Chareyre
  • 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
  • S. Chen
  • T. Chen
  • X. Chen
  • S. Cheng
  • A. Cheplakov
  • V. F. Chepurnov
  • R. Cherkaoui El Moursli
  • V. Chernyatin
  • E. Cheu
  • S. L. Cheung
  • L. Chevalier
  • G. Chiefari
  • L. Chikovani
  • J. T. Childers
  • A. Chilingarov
  • G. Chiodini
  • M. V. Chizhov
  • G. Choudalakis
  • S. Chouridou
  • I. A. Christidi
  • A. Christov
  • D. Chromek-Burckhart
  • M. L. Chu
  • J. Chudoba
  • G. Ciapetti
  • K. Ciba
  • A. K. Ciftci
  • R. Ciftci
  • D. Cinca
  • V. Cindro
  • M. D. Ciobotaru
  • C. Ciocca
  • A. Ciocio
  • M. Cirilli
  • M. Citterio
  • M. Ciubancan
  • A. Clark
  • P. J. Clark
  • W. Cleland
  • J. C. Clemens
  • B. Clement
  • C. Clement
  • R. W. Clifft
  • Y. Coadou
  • M. Cobal
  • A. Coccaro
  • J. Cochran
  • P. Coe
  • J. G. Cogan
  • J. Coggeshall
  • E. Cogneras
  • C. D. Cojocaru
  • J. Colas
  • A. P. Colijn
  • C. Collard
  • N. J. Collins
  • C. Collins-Tooth
  • J. Collot
  • G. Colon
  • P. Conde Muiño
  • E. Coniavitis
  • M. C. Conidi
  • M. Consonni
  • V. Consorti
  • S. Constantinescu
  • C. Conta
  • F. Conventi
  • J. Cook
  • M. Cooke
  • B. D. Cooper
  • A. M. Cooper-Sarkar
  • K. Copic
  • T. Cornelissen
  • M. Corradi
  • F. Corriveau
  • A. Corso-Radu
  • A. Cortes-Gonzalez
  • G. Cortiana
  • G. Costa
  • M. J. Costa
  • D. Costanzo
  • T. Costin
  • D. Côté
  • R. Coura Torres
  • L. Courneyea
  • G. Cowan
  • C. Cowden
  • B. E. Cox
  • K. Cranmer
  • J. Cranshaw
  • F. Crescioli
  • M. Cristinziani
  • G. Crosetti
  • R. Crupi
  • S. Crépé-Renaudin
  • C.-M. Cuciuc
  • C. Cuenca Almenar
  • T. Cuhadar Donszelmann
  • M. Curatolo
  • C. J. Curtis
  • P. Cwetanski
  • H. Czirr
  • Z. Czyczula
  • S. D’Auria
  • M. D’Onofrio
  • A. D’Orazio
  • P. V. M. Da Silva
  • C. Da Via
  • W. Dabrowski
  • T. Dai
  • C. Dallapiccola
  • C. H. Daly
  • M. Dam
  • M. Dameri
  • D. S. Damiani
  • H. O. Danielsson
  • D. Dannheim
  • V. Dao
  • G. Darbo
  • G. L. Darlea
  • C. Daum
  • J. P. Dauvergne
  • W. Davey
  • T. Davidek
  • N. Davidson
  • R. Davidson
  • E. Davies
  • M. Davies
  • A. R. Davison
  • Y. Davygora
  • E. Dawe
  • I. Dawson
  • J. W. Dawson
  • R. K. Daya-Ishmukhametova
  • K. De
  • R. de Asmundis
  • S. De Castro
  • P. E. De Castro Faria Salgado
  • S. De Cecco
  • J. de Graat
  • N. De Groot
  • P. de Jong
  • C. De La Taille
  • H. De la Torre
  • B. De Lotto
  • L. de Mora
  • L. De Nooij
  • D. De Pedis
  • A. De Salvo
  • U. De Sanctis
  • A. De Santo
  • J. B. De Vivie De Regie
  • S. Dean
  • R. Debbe
  • D. V. Dedovich
  • J. Degenhardt
  • M. Dehchar
  • C. Del Papa
  • J. Del Peso
  • T. Del Prete
  • M. Deliyergiyev
  • A. Dell’Acqua
  • L. Dell’Asta
  • M. Della Pietra
  • D. della Volpe
  • M. Delmastro
  • P. Delpierre
  • N. Delruelle
  • P. A. Delsart
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  • M. Demichev
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  • W. Deng
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  • F. Derue
  • P. Dervan
  • K. Desch
  • E. Devetak
  • P. O. Deviveiros
  • A. Dewhurst
  • B. DeWilde
  • S. Dhaliwal
  • R. Dhullipudi
  • A. Di Ciaccio
  • L. Di Ciaccio
  • A. Di Girolamo
  • B. Di Girolamo
  • S. Di Luise
  • A. Di Mattia
  • B. Di Micco
  • R. Di Nardo
  • A. Di Simone
  • R. Di Sipio
  • M. A. Diaz
  • F. Diblen
  • 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
  • M. Dobbs
  • R. Dobinson
  • D. Dobos
  • E. Dobson
  • M. Dobson
  • J. Dodd
  • C. Doglioni
  • T. Doherty
  • Y. Doi
  • J. Dolejsi
  • I. Dolenc
  • Z. Dolezal
  • B. A. Dolgoshein
  • T. Dohmae
  • M. Donadelli
  • M. Donega
  • J. Donini
  • J. Dopke
  • A. Doria
  • A. Dos Anjos
  • M. Dosil
  • A. Dotti
  • M. T. Dova
  • J. D. Dowell
  • A. D. Doxiadis
  • A. T. Doyle
  • Z. Drasal
  • J. Drees
  • N. Dressnandt
  • H. Drevermann
  • C. Driouichi
  • M. Dris
  • J. Dubbert
  • T. Dubbs
  • S. Dube
  • E. Duchovni
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  • A. Dudarev
  • F. Dudziak
  • M. Dührssen
  • I. P. Duerdoth
  • L. Duflot
  • M.-A. Dufour
  • M. Dunford
  • H. Duran Yildiz
  • R. Duxfield
  • M. Dwuznik
  • F. Dydak
  • M. Düren
  • W. L. Ebenstein
  • J. Ebke
  • S. Eckert
  • S. Eckweiler
  • K. Edmonds
  • C. A. Edwards
  • N. C. Edwards
  • W. Ehrenfeld
  • T. Ehrich
  • 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
  • R. Engelmann
  • A. Engl
  • B. Epp
  • A. Eppig
  • J. Erdmann
  • A. Ereditato
  • D. Eriksson
  • J. Ernst
  • M. Ernst
  • J. Ernwein
  • D. Errede
  • S. Errede
  • E. Ertel
  • M. Escalier
  • C. Escobar
  • X. Espinal Curull
  • B. Esposito
  • F. Etienne
  • A. I. Etienvre
  • E. Etzion
  • D. Evangelakou
  • H. Evans
  • L. Fabbri
  • C. Fabre
  • R. M. Fakhrutdinov
  • S. Falciano
  • Y. Fang
  • M. Fanti
  • A. Farbin
  • A. Farilla
  • J. Farley
  • T. Farooque
  • S. M. Farrington
  • P. Farthouat
  • P. Fassnacht
  • D. Fassouliotis
  • B. Fatholahzadeh
  • A. Favareto
  • L. Fayard
  • S. Fazio
  • R. Febbraro
  • P. Federic
  • O. L. Fedin
  • W. Fedorko
  • M. Fehling-Kaschek
  • L. Feligioni
  • D. Fellmann
  • C. U. Felzmann
  • C. Feng
  • E. J. Feng
  • A. B. Fenyuk
  • J. Ferencei
  • J. Ferland
  • W. Fernando
  • S. Ferrag
  • J. Ferrando
  • V. Ferrara
  • A. Ferrari
  • P. Ferrari
  • R. Ferrari
  • A. Ferrer
  • M. L. Ferrer
  • D. Ferrere
  • C. Ferretti
  • A. Ferretto Parodi
  • M. Fiascaris
  • F. Fiedler
  • A. Filipčič
  • A. Filippas
  • F. Filthaut
  • M. Fincke-Keeler
  • M. C. N. Fiolhais
  • L. Fiorini
  • A. Firan
  • G. Fischer
  • P. Fischer
  • M. J. Fisher
  • S. M. Fisher
  • M. Flechl
  • I. Fleck
  • J. Fleckner
  • P. Fleischmann
  • S. Fleischmann
  • T. Flick
  • L. R. Flores Castillo
  • M. J. Flowerdew
  • M. Fokitis
  • T. Fonseca Martin
  • J. Fopma
  • D. A. Forbush
  • A. Formica
  • A. Forti
  • D. Fortin
  • J. M. Foster
  • D. Fournier
  • A. Foussat
  • A. J. Fowler
  • K. Fowler
  • H. Fox
  • P. Francavilla
  • S. Franchino
  • D. Francis
  • T. Frank
  • M. Franklin
  • S. Franz
  • M. Fraternali
  • S. Fratina
  • J. Freestone
  • S. T. French
  • F. Friedrich
  • R. Froeschl
  • D. Froidevaux
  • J. A. Frost
  • C. Fukunaga
  • E. Fullana Torregrosa
  • J. Fuster
  • C. Gabaldon
  • O. Gabizon
  • T. Gadfort
  • S. Gadomski
  • G. Gagliardi
  • P. Gagnon
  • C. Galea
  • E. J. Gallas
  • V. Gallo
  • B. J. Gallop
  • P. Gallus
  • E. Galyaev
  • K. K. Gan
  • Y. S. Gao
  • V. A. Gapienko
  • A. Gaponenko
  • F. Garberson
  • M. Garcia-Sciveres
  • C. García
  • J. E. García Navarro
  • R. W. Gardner
  • N. Garelli
  • H. Garitaonandia
  • V. Garonne
  • J. Garvey
  • C. Gatti
  • G. Gaudio
  • O. Gaumer
  • B. Gaur
  • L. Gauthier
  • P. Gauzzi
  • I. L. Gavrilenko
  • C. Gay
  • G. Gaycken
  • J-C. Gayde
  • E. N. Gazis
  • P. Ge
  • 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
  • P. Gerlach
  • A. Gershon
  • C. Geweniger
  • H. Ghazlane
  • N. Ghodbane
  • B. Giacobbe
  • S. Giagu
  • V. Giakoumopoulou
  • V. Giangiobbe
  • F. Gianotti
  • B. Gibbard
  • A. Gibson
  • S. M. Gibson
  • L. M. Gilbert
  • M. Gilchriese
  • V. Gilewsky
  • D. Gillberg
  • A. R. Gillman
  • D. M. Gingrich
  • J. Ginzburg
  • N. Giokaris
  • M. P. Giordani
  • R. Giordano
  • F. M. Giorgi
  • P. Giovannini
  • P. F. Giraud
  • D. Giugni
  • M. Giunta
  • P. Giusti
  • B. K. Gjelsten
  • L. K. Gladilin
  • C. Glasman
  • J. Glatzer
  • A. Glazov
  • K. W. Glitza
  • G. L. Glonti
  • J. Godfrey
  • J. Godlewski
  • M. Goebel
  • T. Göpfert
  • C. Goeringer
  • C. Gössling
  • T. Göttfert
  • S. Goldfarb
  • T. Golling
  • S. N. Golovnia
  • A. Gomes
  • L. S. Gomez Fajardo
  • R. Gonçalo
  • J. Goncalves Pinto Firmino Da Costa
  • L. Gonella
  • A. Gonidec
  • S. Gonzalez
  • S. González de la Hoz
  • 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
  • S. A. Gorokhov
  • V. N. Goryachev
  • B. Gosdzik
  • M. Gosselink
  • M. I. Gostkin
  • I. Gough Eschrich
  • M. Gouighri
  • D. Goujdami
  • M. P. Goulette
  • A. G. Goussiou
  • C. Goy
  • I. Grabowska-Bold
  • P. Grafström
  • C. Grah
  • K-J. Grahn
  • F. Grancagnolo
  • S. Grancagnolo
  • V. Grassi
  • V. Gratchev
  • N. Grau
  • H. M. Gray
  • J. A. Gray
  • E. Graziani
  • O. G. Grebenyuk
  • B. Green
  • D. Greenfield
  • T. Greenshaw
  • Z. D. Greenwood
  • K. Gregersen
  • I. M. Gregor
  • P. Grenier
  • J. Griffiths
  • N. Grigalashvili
  • A. A. Grillo
  • S. Grinstein
  • Y. V. Grishkevich
  • J.-F. Grivaz
  • M. Groh
  • E. Gross
  • J. Grosse-Knetter
  • J. Groth-Jensen
  • K. Grybel
  • V. J. Guarino
  • D. Guest
  • C. Guicheney
  • A. Guida
  • S. Guindon
  • H. Guler
  • J. Gunther
  • B. Guo
  • J. Guo
  • A. Gupta
  • Y. Gusakov
  • V. N. Gushchin
  • A. Gutierrez
  • P. Gutierrez
  • N. Guttman
  • O. Gutzwiller
  • C. Guyot
  • C. Gwenlan
  • C. B. Gwilliam
  • A. Haas
  • S. Haas
  • C. Haber
  • R. Hackenburg
  • H. K. Hadavand
  • D. R. Hadley
  • P. Haefner
  • F. Hahn
  • S. Haider
  • Z. Hajduk
  • H. Hakobyan
  • J. Haller
  • K. Hamacher
  • P. Hamal
  • A. Hamilton
  • S. Hamilton
  • H. Han
  • L. Han
  • K. Hanagaki
  • M. Hance
  • C. Handel
  • P. Hanke
  • J. R. Hansen
  • J. B. Hansen
  • J. D. Hansen
  • P. H. Hansen
  • P. Hansson
  • K. Hara
  • G. A. Hare
  • T. Harenberg
  • S. Harkusha
  • D. Harper
  • R. D. Harrington
  • O. M. Harris
  • K. Harrison
  • J. Hartert
  • F. Hartjes
  • T. Haruyama
  • A. Harvey
  • S. Hasegawa
  • Y. Hasegawa
  • S. Hassani
  • M. Hatch
  • D. Hauff
  • S. Haug
  • M. Hauschild
  • R. Hauser
  • M. Havranek
  • B. M. Hawes
  • C. M. Hawkes
  • R. J. Hawkings
  • D. Hawkins
  • T. Hayakawa
  • T. Hayashi
  • D. Hayden
  • H. S. Hayward
  • S. J. Haywood
  • E. Hazen
  • M. He
  • S. J. Head
  • V. Hedberg
  • L. Heelan
  • S. Heim
  • B. Heinemann
  • S. Heisterkamp
  • L. Helary
  • M. Heller
  • S. Hellman
  • D. Hellmich
  • C. Helsens
  • T. Hemperek
  • R. C. W. Henderson
  • M. Henke
  • A. Henrichs
  • A. M. Henriques Correia
  • S. Henrot-Versille
  • F. Henry-Couannier
  • C. Hensel
  • T. Henß
  • C. M. Hernandez
  • Y. Hernández Jiménez
  • R. Herrberg
  • A. D. Hershenhorn
  • G. Herten
  • R. Hertenberger
  • L. Hervas
  • N. P. Hessey
  • A. Hidvegi
  • E. Higón-Rodriguez
  • D. Hill
  • J. C. Hill
  • N. Hill
  • K. H. Hiller
  • S. Hillert
  • S. J. Hillier
  • I. Hinchliffe
  • E. Hines
  • M. Hirose
  • F. Hirsch
  • D. Hirschbuehl
  • J. Hobbs
  • N. Hod
  • M. C. Hodgkinson
  • P. Hodgson
  • A. Hoecker
  • M. R. Hoeferkamp
  • J. Hoffman
  • D. Hoffmann
  • M. Hohlfeld
  • M. Holder
  • S. O. Holmgren
  • T. Holy
  • J. L. Holzbauer
  • Y. Homma
  • T. M. Hong
  • L. Hooft van Huysduynen
  • T. Horazdovsky
  • C. Horn
  • S. Horner
  • K. Horton
  • J-Y. Hostachy
  • S. Hou
  • M. A. Houlden
  • A. Hoummada
  • J. Howarth
  • D. F. Howell
  • I. Hristova
  • J. Hrivnac
  • I. Hruska
  • T. Hryn’ova
  • P. J. Hsu
  • S.-C. Hsu
  • G. S. Huang
  • Z. Hubacek
  • F. Hubaut
  • F. Huegging
  • T. B. Huffman
  • E. W. Hughes
  • G. Hughes
  • R. E. Hughes-Jones
  • M. Huhtinen
  • P. Hurst
  • M. Hurwitz
  • U. Husemann
  • N. Huseynov
  • J. Huston
  • J. Huth
  • G. Iacobucci
  • G. Iakovidis
  • M. Ibbotson
  • I. Ibragimov
  • R. Ichimiya
  • L. Iconomidou-Fayard
  • J. Idarraga
  • P. Iengo
  • O. Igonkina
  • Y. Ikegami
  • M. Ikeno
  • Y. Ilchenko
  • D. Iliadis
  • D. Imbault
  • M. Imori
  • T. Ince
  • J. Inigo-Golfin
  • P. Ioannou
  • M. Iodice
  • A. Irles Quiles
  • A. Ishikawa
  • M. Ishino
  • R. Ishmukhametov
  • C. Issever
  • S. Istin
  • A. V. Ivashin
  • W. Iwanski
  • H. Iwasaki
  • J. M. Izen
  • V. Izzo
  • B. Jackson
  • J. N. Jackson
  • P. Jackson
  • M. R. Jaekel
  • V. Jain
  • K. Jakobs
  • S. Jakobsen
  • J. Jakubek
  • D. K. Jana
  • E. Jankowski
  • E. Jansen
  • A. Jantsch
  • M. Janus
  • G. Jarlskog
  • L. Jeanty
  • K. Jelen
  • I. Jen-La Plante
  • P. Jenni
  • A. Jeremie
  • P. Jež
  • S. Jézéquel
  • M. K. Jha
  • H. Ji
  • W. Ji
  • J. Jia
  • Y. Jiang
  • M. Jimenez Belenguer
  • G. Jin
  • S. Jin
  • O. Jinnouchi
  • M. D. Joergensen
  • D. Joffe
  • L. G. Johansen
  • M. Johansen
  • K. E. Johansson
  • P. Johansson
  • S. Johnert
  • K. A. Johns
  • K. Jon-And
  • G. Jones
  • R. W. L. Jones
  • T. W. Jones
  • T. J. Jones
  • O. Jonsson
  • C. Joram
  • P. M. Jorge
  • J. Joseph
  • T. Jovin
  • X. Ju
  • C. A. Jung
  • V. Juranek
  • P. Jussel
  • A. Juste Rozas
  • V. V. Kabachenko
  • S. Kabana
  • M. Kaci
  • A. Kaczmarska
  • P. Kadlecik
  • M. Kado
  • H. Kagan
  • M. Kagan
  • S. Kaiser
  • E. Kajomovitz
  • S. Kalinin
  • L. V. Kalinovskaya
  • S. Kama
  • N. Kanaya
  • M. Kaneda
  • T. Kanno
  • V. A. Kantserov
  • J. Kanzaki
  • B. Kaplan
  • A. Kapliy
  • J. Kaplon
  • D. Kar
  • M. Karagounis
  • M. Karagoz
  • M. Karnevskiy
  • K. Karr
  • V. Kartvelishvili
  • A. N. Karyukhin
  • L. Kashif
  • A. Kasmi
  • R. D. Kass
  • A. Kastanas
  • M. Kataoka
  • Y. Kataoka
  • E. Katsoufis
  • J. Katzy
  • V. Kaushik
  • K. Kawagoe
  • T. Kawamoto
  • G. Kawamura
  • M. S. Kayl
  • V. A. Kazanin
  • M. Y. Kazarinov
  • J. R. Keates
  • R. Keeler
  • R. Kehoe
  • M. Keil
  • G. D. Kekelidze
  • M. Kelly
  • J. Kennedy
  • C. J. Kenney
  • M. Kenyon
  • O. Kepka
  • N. Kerschen
  • B. P. Kerševan
  • S. Kersten
  • K. Kessoku
  • C. Ketterer
  • J. Keung
  • M. Khakzad
  • F. Khalil-zada
  • H. Khandanyan
  • A. Khanov
  • D. Kharchenko
  • A. Khodinov
  • A. G. Kholodenko
  • A. Khomich
  • T. J. Khoo
  • G. Khoriauli
  • A. Khoroshilov
  • N. Khovanskiy
  • V. Khovanskiy
  • E. Khramov
  • J. Khubua
  • H. Kim
  • M. S. Kim
  • P. C. Kim
  • S. H. Kim
  • N. Kimura
  • O. Kind
  • B. T. King
  • M. King
  • R. S. B. King
  • J. Kirk
  • L. E. Kirsch
  • A. E. Kiryunin
  • T. Kishimoto
  • D. Kisielewska
  • T. Kittelmann
  • A. M. Kiver
  • E. Kladiva
  • J. Klaiber-Lodewigs
  • M. Klein
  • U. Klein
  • K. Kleinknecht
  • M. Klemetti
  • A. Klier
  • A. Klimentov
  • R. Klingenberg
  • E. B. Klinkby
  • T. Klioutchnikova
  • P. F. Klok
  • S. Klous
  • E.-E. Kluge
  • T. Kluge
  • P. Kluit
  • S. Kluth
  • N. S. Knecht
  • E. Kneringer
  • J. Knobloch
  • E. B. F. G. Knoops
  • A. Knue
  • B. R. Ko
  • T. Kobayashi
  • M. Kobel
  • M. Kocian
  • A. Kocnar
  • P. Kodys
  • K. Köneke
  • A. C. König
  • S. Koenig
  • L. Köpke
  • F. Koetsveld
  • P. Koevesarki
  • T. Koffas
  • E. Koffeman
  • F. Kohn
  • Z. Kohout
  • T. Kohriki
  • T. Koi
  • T. Kokott
  • G. M. Kolachev
  • H. Kolanoski
  • V. Kolesnikov
  • I. Koletsou
  • J. Koll
  • D. Kollar
  • M. Kollefrath
  • S. D. Kolya
  • A. A. Komar
  • Y. Komori
  • T. Kondo
  • T. Kono
  • A. I. Kononov
  • R. Konoplich
  • N. Konstantinidis
  • A. Kootz
  • S. Koperny
  • S. V. Kopikov
  • K. Korcyl
  • K. Kordas
  • V. Koreshev
  • A. Korn
  • A. Korol
  • I. Korolkov
  • E. V. Korolkova
  • V. A. Korotkov
  • O. Kortner
  • S. Kortner
  • V. V. Kostyukhin
  • M. J. Kotamäki
  • 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. Kraus
  • J. K. Kraus
  • A. Kreisel
  • F. Krejci
  • J. Kretzschmar
  • N. Krieger
  • P. Krieger
  • K. Kroeninger
  • H. Kroha
  • J. Kroll
  • J. Kroseberg
  • J. Krstic
  • U. Kruchonak
  • H. Krüger
  • T. Kruker
  • Z. V. Krumshteyn
  • A. Kruth
  • T. Kubota
  • S. Kuehn
  • A. Kugel
  • T. Kuhl
  • D. Kuhn
  • V. Kukhtin
  • Y. Kulchitsky
  • S. Kuleshov
  • C. Kummer
  • M. Kuna
  • N. Kundu
  • J. Kunkle
  • A. Kupco
  • H. Kurashige
  • M. Kurata
  • Y. A. Kurochkin
  • V. Kus
  • M. Kuze
  • P. Kuzhir
  • J. Kvita
  • R. Kwee
  • A. La Rosa
  • L. La Rotonda
  • L. Labarga
  • J. Labbe
  • S. Lablak
  • C. Lacasta
  • F. Lacava
  • H. Lacker
  • D. Lacour
  • V. R. Lacuesta
  • E. Ladygin
  • R. Lafaye
  • B. Laforge
  • T. Lagouri
  • S. Lai
  • E. Laisne
  • M. Lamanna
  • C. L. Lampen
  • W. Lampl
  • E. Lancon
  • U. Landgraf
  • M. P. J. Landon
  • H. Landsman
  • J. L. Lane
  • C. Lange
  • A. J. Lankford
  • F. Lanni
  • K. Lantzsch
  • A. Lanza
  • S. Laplace
  • C. Lapoire
  • J. F. Laporte
  • T. Lari
  • A. V. Larionov
  • A. Larner
  • C. Lasseur
  • M. Lassnig
  • P. Laurelli
  • W. Lavrijsen
  • P. Laycock
  • A. B. Lazarev
  • O. Le Dortz
  • E. Le Guirriec
  • C. Le Maner
  • E. Le Menedeu
  • C. Lebel
  • T. LeCompte
  • F. Ledroit-Guillon
  • H. Lee
  • J. S. H. Lee
  • S. C. Lee
  • L. Lee
  • M. Lefebvre
  • M. Legendre
  • A. Leger
  • B. C. LeGeyt
  • F. Legger
  • C. Leggett
  • M. Lehmacher
  • G. Lehmann Miotto
  • X. Lei
  • M. A. L. Leite
  • R. Leitner
  • D. Lellouch
  • M. Leltchouk
  • B. Lemmer
  • V. Lendermann
  • K. J. C. Leney
  • T. Lenz
  • G. Lenzen
  • B. Lenzi
  • K. Leonhardt
  • S. Leontsinis
  • C. Leroy
  • J-R. Lessard
  • J. Lesser
  • C. G. Lester
  • A. Leung Fook Cheong
  • J. Levêque
  • D. Levin
  • L. J. Levinson
  • M. S. Levitski
  • M. Lewandowska
  • A. Lewis
  • G. H. Lewis
  • A. M. Leyko
  • M. Leyton
  • B. Li
  • H. Li
  • S. Li
  • X. Li
  • Z. Liang
  • Z. Liang
  • H. Liao
  • B. Liberti
  • P. Lichard
  • M. Lichtnecker
  • K. Lie
  • W. Liebig
  • R. Lifshitz
  • J. N. Lilley
  • C. Limbach
  • A. Limosani
  • M. Limper
  • S. C. Lin
  • F. Linde
  • J. T. Linnemann
  • E. Lipeles
  • L. Lipinsky
  • A. Lipniacka
  • T. M. Liss
  • D. Lissauer
  • A. Lister
  • A. M. Litke
  • C. Liu
  • D. Liu
  • H. Liu
  • J. B. Liu
  • M. Liu
  • S. Liu
  • Y. Liu
  • M. Livan
  • S. S. A. Livermore
  • A. Lleres
  • J. Llorente Merino
  • S. L. Lloyd
  • E. Lobodzinska
  • P. Loch
  • W. S. Lockman
  • T. Loddenkoetter
  • F. K. Loebinger
  • A. Loginov
  • C. W. Loh
  • T. Lohse
  • K. Lohwasser
  • M. Lokajicek
  • J. Loken
  • V. P. Lombardo
  • R. E. Long
  • L. Lopes
  • D. Lopez Mateos
  • M. Losada
  • P. Loscutoff
  • F. Lo Sterzo
  • M. J. Losty
  • X. Lou
  • A. Lounis
  • K. F. Loureiro
  • J. Love
  • P. A. Love
  • A. J. Lowe
  • F. Lu
  • H. J. Lubatti
  • C. Luci
  • A. Lucotte
  • A. Ludwig
  • D. Ludwig
  • I. Ludwig
  • J. Ludwig
  • F. Luehring
  • G. Luijckx
  • D. Lumb
  • L. Luminari
  • E. Lund
  • B. Lund-Jensen
  • B. Lundberg
  • J. Lundberg
  • J. Lundquist
  • M. Lungwitz
  • A. Lupi
  • G. Lutz
  • D. Lynn
  • J. Lys
  • E. Lytken
  • H. Ma
  • L. L. Ma
  • J. A. Macana Goia
  • G. Maccarrone
  • A. Macchiolo
  • B. Maček
  • J. Machado Miguens
  • R. Mackeprang
  • R. J. Madaras
  • W. F. Mader
  • R. Maenner
  • T. Maeno
  • P. Mättig
  • S. Mättig
  • L. Magnoni
  • E. Magradze
  • Y. Mahalalel
  • K. Mahboubi
  • G. Mahout
  • C. Maiani
  • C. Maidantchik
  • A. Maio
  • S. Majewski
  • Y. Makida
  • N. Makovec
  • P. Mal
  • B. Malaescu
  • Pa. Malecki
  • P. Malecki
  • V. P. Maleev
  • F. Malek
  • U. Mallik
  • D. Malon
  • C. Malone
  • S. Maltezos
  • V. Malyshev
  • S. Malyukov
  • R. Mameghani
  • J. Mamuzic
  • A. Manabe
  • L. Mandelli
  • I. Mandić
  • R. Mandrysch
  • J. Maneira
  • P. S. Mangeard
  • I. D. Manjavidze
  • A. Mann
  • P. M. Manning
  • A. Manousakis-Katsikakis
  • B. Mansoulie
  • A. Manz
  • A. Mapelli
  • L. Mapelli
  • L. March
  • J. F. Marchand
  • F. Marchese
  • G. Marchiori
  • M. Marcisovsky
  • A. Marin
  • C. P. Marino
  • F. Marroquim
  • R. Marshall
  • Z. Marshall
  • F. K. Martens
  • S. Marti-Garcia
  • A. J. Martin
  • B. Martin
  • B. Martin
  • F. F. Martin
  • J. P. Martin
  • Ph. Martin
  • T. A. Martin
  • V. J. Martin
  • B. Martin dit Latour
  • S. Martin-Haugh
  • M. Martinez
  • V. Martinez Outschoorn
  • A. C. Martyniuk
  • M. Marx
  • F. Marzano
  • A. Marzin
  • L. Masetti
  • T. Mashimo
  • R. Mashinistov
  • J. Masik
  • A. L. Maslennikov
  • I. Massa
  • G. Massaro
  • N. Massol
  • P. Mastrandrea
  • A. Mastroberardino
  • T. Masubuchi
  • M. Mathes
  • P. Matricon
  • H. Matsumoto
  • H. Matsunaga
  • T. Matsushita
  • C. Mattravers
  • J. M. Maugain
  • S. J. Maxfield
  • D. A. Maximov
  • E. N. May
  • A. Mayne
  • R. Mazini
  • M. Mazur
  • M. Mazzanti
  • E. Mazzoni
  • S. P. Mc Kee
  • A. McCarn
  • R. L. McCarthy
  • T. G. McCarthy
  • N. A. McCubbin
  • K. W. McFarlane
  • J. A. Mcfayden
  • H. McGlone
  • G. Mchedlidze
  • R. A. McLaren
  • T. Mclaughlan
  • S. J. McMahon
  • R. A. McPherson
  • A. Meade
  • J. Mechnich
  • M. Mechtel
  • M. Medinnis
  • R. Meera-Lebbai
  • T. Meguro
  • R. Mehdiyev
  • S. Mehlhase
  • A. Mehta
  • K. Meier
  • J. Meinhardt
  • B. Meirose
  • C. Melachrinos
  • B. R. Mellado Garcia
  • L. Mendoza Navas
  • Z. Meng
  • A. Mengarelli
  • S. Menke
  • C. Menot
  • E. Meoni
  • K. M. Mercurio
  • P. Mermod
  • L. Merola
  • C. Meroni
  • F. S. Merritt
  • A. Messina
  • J. Metcalfe
  • A. S. Mete
  • C. Meyer
  • J-P. Meyer
  • J. Meyer
  • J. Meyer
  • T. C. Meyer
  • W. T. Meyer
  • J. Miao
  • S. Michal
  • L. Micu
  • R. P. Middleton
  • P. Miele
  • S. Migas
  • L. Mijović
  • G. Mikenberg
  • M. Mikestikova
  • M. Mikuž
  • D. W. Miller
  • R. J. Miller
  • W. J. Mills
  • 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
  • L. Miralles Verge
  • S. Misawa
  • A. Misiejuk
  • J. Mitrevski
  • G. Y. Mitrofanov
  • V. A. Mitsou
  • S. Mitsui
  • P. S. Miyagawa
  • K. Miyazaki
  • J. U. Mjörnmark
  • T. Moa
  • P. Mockett
  • S. Moed
  • V. Moeller
  • K. Mönig
  • N. Möser
  • S. Mohapatra
  • W. Mohr
  • S. Mohrdieck-Möck
  • A. M. Moisseev
  • R. Moles-Valls
  • J. Molina-Perez
  • J. Monk
  • E. Monnier
  • S. Montesano
  • F. Monticelli
  • S. Monzani
  • R. W. Moore
  • G. F. Moorhead
  • C. Mora Herrera
  • A. Moraes
  • N. Morange
  • J. Morel
  • G. Morello
  • D. Moreno
  • M. Moreno Llácer
  • P. Morettini
  • M. Morii
  • J. Morin
  • A. K. Morley
  • G. Mornacchi
  • S. V. Morozov
  • J. D. Morris
  • L. Morvaj
  • H. G. Moser
  • M. Mosidze
  • J. Moss
  • R. Mount
  • E. Mountricha
  • S. V. Mouraviev
  • E. J. W. Moyse
  • M. Mudrinic
  • F. Mueller
  • J. Mueller
  • K. Mueller
  • T. A. Müller
  • D. Muenstermann
  • A. Muir
  • Y. Munwes
  • W. J. Murray
  • I. Mussche
  • E. Musto
  • A. G. Myagkov
  • M. Myska
  • J. Nadal
  • K. Nagai
  • K. Nagano
  • Y. Nagasaka
  • A. M. Nairz
  • Y. Nakahama
  • K. Nakamura
  • T. Nakamura
  • I. Nakano
  • G. Nanava
  • A. Napier
  • M. Nash
  • N. R. Nation
  • T. Nattermann
  • T. Naumann
  • G. Navarro
  • H. A. Neal
  • E. Nebot
  • P. Yu. Nechaeva
  • A. Negri
  • G. Negri
  • S. Nektarijevic
  • A. Nelson
  • S. Nelson
  • T. K. Nelson
  • S. Nemecek
  • P. Nemethy
  • A. A. Nepomuceno
  • M. Nessi
  • S. Y. Nesterov
  • M. S. Neubauer
  • A. Neusiedl
  • R. M. Neves
  • P. Nevski
  • P. R. Newman
  • V. Nguyen Thi Hong
  • R. B. Nickerson
  • R. Nicolaidou
  • L. Nicolas
  • G. Nicoletti
  • B. Nicquevert
  • F. Niedercorn
  • J. Nielsen
  • T. Niinikoski
  • N. Nikiforou
  • A. Nikiforov
  • V. Nikolaenko
  • K. Nikolaev
  • I. Nikolic-Audit
  • K. Nikolics
  • K. Nikolopoulos
  • H. Nilsen
  • P. Nilsson
  • Y. Ninomiya
  • A. Nisati
  • T. Nishiyama
  • R. Nisius
  • L. Nodulman
  • M. Nomachi
  • I. Nomidis
  • M. Nordberg
  • B. Nordkvist
  • P. R. Norton
  • D. Notz
  • J. Novakova
  • M. Nozaki
  • L. Nozka
  • I. M. Nugent
  • A.-E. Nuncio-Quiroz
  • G. Nunes Hanninger
  • T. Nunnemann
  • E. Nurse
  • T. Nyman
  • B. J. O’Brien
  • S. W. O’Neale
  • D. C. O’Neil
  • V. O’Shea
  • F. G. Oakham
  • H. Oberlack
  • J. Ocariz
  • A. Ochi
  • S. Oda
  • S. Odaka
  • J. Odier
  • H. Ogren
  • A. Oh
  • S. H. Oh
  • C. C. Ohm
  • T. Ohshima
  • H. Ohshita
  • T. Ohsugi
  • S. Okada
  • H. Okawa
  • Y. Okumura
  • T. Okuyama
  • M. Olcese
  • A. G. Olchevski
  • M. Oliveira
  • D. Oliveira Damazio
  • E. Oliver Garcia
  • D. Olivito
  • A. Olszewski
  • J. Olszowska
  • C. Omachi
  • A. Onofre
  • P. U. E. Onyisi
  • C. J. Oram
  • M. J. Oreglia
  • Y. Oren
  • D. Orestano
  • I. Orlov
  • C. Oropeza Barrera
  • R. S. Orr
  • B. Osculati
  • R. Ospanov
  • C. Osuna
  • G. Otero y Garzon
  • J. P. Ottersbach
  • M. Ouchrif
  • F. Ould-Saada
  • A. Ouraou
  • Q. Ouyang
  • M. Owen
  • S. Owen
  • V. E. Ozcan
  • N. Ozturk
  • A. Pacheco Pages
  • C. Padilla Aranda
  • S. Pagan Griso
  • E. Paganis
  • F. Paige
  • K. Pajchel
  • G. Palacino
  • C. P. Paleari
  • S. Palestini
  • D. Pallin
  • A. Palma
  • J. D. Palmer
  • Y. B. Pan
  • E. Panagiotopoulou
  • B. Panes
  • N. Panikashvili
  • S. Panitkin
  • D. Pantea
  • M. Panuskova
  • V. Paolone
  • A. Papadelis
  • Th. D. Papadopoulou
  • A. Paramonov
  • W. Park
  • M. A. Parker
  • F. Parodi
  • J. A. Parsons
  • U. Parzefall
  • E. Pasqualucci
  • A. Passeri
  • F. Pastore
  • Fr. Pastore
  • G. Pásztor
  • S. Pataraia
  • N. Patel
  • J. R. Pater
  • S. Patricelli
  • T. Pauly
  • M. Pecsy
  • M. I. Pedraza Morales
  • S. V. Peleganchuk
  • H. Peng
  • R. Pengo
  • A. Penson
  • J. Penwell
  • M. Perantoni
  • K. Perez
  • T. Perez Cavalcanti
  • E. Perez Codina
  • M. T. Pérez García-Estañ
  • V. Perez Reale
  • L. Perini
  • H. Pernegger
  • R. Perrino
  • P. Perrodo
  • S. Persembe
  • A. Perus
  • V. D. Peshekhonov
  • B. A. Petersen
  • J. Petersen
  • T. C. Petersen
  • E. Petit
  • A. Petridis
  • C. Petridou
  • E. Petrolo
  • F. Petrucci
  • D. Petschull
  • M. Petteni
  • R. Pezoa
  • B. Pfeifer
  • A. Phan
  • A. W. Phillips
  • P. W. Phillips
  • G. Piacquadio
  • E. Piccaro
  • M. Piccinini
  • A. Pickford
  • S. M. Piec
  • R. Piegaia
  • J. E. Pilcher
  • A. D. Pilkington
  • J. Pina
  • M. Pinamonti
  • A. Pinder
  • J. L. Pinfold
  • J. Ping
  • B. Pinto
  • O. Pirotte
  • C. Pizio
  • R. Placakyte
  • M. Plamondon
  • M.-A. Pleier
  • A. V. Pleskach
  • A. Poblaguev
  • S. Poddar
  • F. Podlyski
  • L. Poggioli
  • T. Poghosyan
  • M. Pohl
  • F. Polci
  • G. Polesello
  • A. Policicchio
  • A. Polini
  • J. Poll
  • V. Polychronakos
  • D. M. Pomarede
  • D. Pomeroy
  • K. Pommès
  • L. Pontecorvo
  • B. G. Pope
  • G. A. Popeneciu
  • D. S. Popovic
  • A. Poppleton
  • X. Portell Bueso
  • C. Posch
  • G. E. Pospelov
  • S. Pospisil
  • M. Potekhin
  • I. N. Potrap
  • C. J. Potter
  • K. P. Potter
  • C. T. Potter
  • G. Poulard
  • J. Poveda
  • R. Prabhu
  • P. Pralavorio
  • S. Prasad
  • R. Pravahan
  • S. Prell
  • K. Pretzl
  • L. Pribyl
  • D. Price
  • L. E. Price
  • M. J. Price
  • P. M. Prichard
  • D. Prieur
  • M. Primavera
  • K. Prokofiev
  • F. Prokoshin
  • S. Protopopescu
  • J. Proudfoot
  • X. Prudent
  • H. Przysiezniak
  • S. Psoroulas
  • E. Ptacek
  • E. Pueschel
  • J. Purdham
  • M. Purohit
  • P. Puzo
  • Y. Pylypchenko
  • J. Qian
  • W. Qian
  • Z. Qian
  • Z. Qin
  • A. Quadt
  • D. R. Quarrie
  • W. B. Quayle
  • F. Quinonez
  • M. Raas
  • V. Radeka
  • V. Radescu
  • B. Radics
  • T. Rador
  • F. Ragusa
  • G. Rahal
  • A. M. Rahimi
  • D. Rahm
  • S. Rajagopalan
  • M. Rammensee
  • M. Rammes
  • M. Ramstedt
  • A. S. Randle-Conde
  • K. Randrianarivony
  • P. N. Ratoff
  • F. Rauscher
  • E. Rauter
  • M. Raymond
  • A. L. Read
  • D. M. Rebuzzi
  • A. Redelbach
  • G. Redlinger
  • R. Reece
  • K. Reeves
  • A. Reichold
  • E. Reinherz-Aronis
  • A. Reinsch
  • I. Reisinger
  • D. Reljic
  • C. Rembser
  • Z. L. Ren
  • A. Renaud
  • P. Renkel
  • S. Rescia
  • M. Rescigno
  • S. Resconi
  • B. Resende
  • P. Reznicek
  • R. Rezvani
  • A. Richards
  • R. Richter
  • E. Richter-Was
  • M. Ridel
  • S. Rieke
  • M. Rijpstra
  • M. Rijssenbeek
  • A. Rimoldi
  • L. Rinaldi
  • R. R. Rios
  • I. Riu
  • G. Rivoltella
  • F. Rizatdinova
  • E. Rizvi
  • S. H. Robertson
  • A. Robichaud-Veronneau
  • D. Robinson
  • J. E. M. Robinson
  • M. Robinson
  • A. Robson
  • J. G. Rocha de Lima
  • C. Roda
  • D. Roda Dos Santos
  • S. Rodier
  • D. Rodriguez
  • Y. Rodriguez Garcia
  • A. Roe
  • S. Roe
  • O. Røhne
  • V. Rojo
  • S. Rolli
  • A. Romaniouk
  • M. Romano
  • V. M. Romanov
  • G. Romeo
  • L. Roos
  • E. Ros
  • S. Rosati
  • K. Rosbach
  • A. Rose
  • M. Rose
  • G. A. Rosenbaum
  • E. I. Rosenberg
  • P. L. Rosendahl
  • O. Rosenthal
  • L. Rosselet
  • V. Rossetti
  • E. Rossi
  • L. P. Rossi
  • L. Rossi
  • M. Rotaru
  • I. Roth
  • J. Rothberg
  • D. Rousseau
  • C. R. Royon
  • A. Rozanov
  • Y. Rozen
  • X. Ruan
  • I. Rubinskiy
  • B. Ruckert
  • N. Ruckstuhl
  • V. I. Rud
  • C. Rudolph
  • G. Rudolph
  • F. Rühr
  • F. Ruggieri
  • A. Ruiz-Martinez
  • E. Rulikowska-Zarebska
  • V. Rumiantsev
  • L. Rumyantsev
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Open Access
Regular Article - Experimental Physics

Abstract

The jet energy scale and its systematic uncertainty are determined for jets measured with the ATLAS detector at the LHC in proton-proton collision data at a centre-of-mass energy of \(\sqrt{s}=7\ \mathrm{TeV}\) corresponding to an integrated luminosity of 38 pb−1. Jets are reconstructed with the anti-kt algorithm with distance parameters R=0.4 or R=0.6. Jet energy and angle corrections are determined from Monte Carlo simulations to calibrate jets with transverse momenta pT≥20 GeV and pseudorapidities |η|<4.5. The jet energy systematic uncertainty is estimated using the single isolated hadron response measured in situ and in test-beams, exploiting the transverse momentum balance between central and forward jets in events with dijet topologies and studying systematic variations in Monte Carlo simulations. The jet energy uncertainty is less than 2.5 % in the central calorimeter region (|η|<0.8) for jets with 60≤pT<800 GeV, and is maximally 14 % for pT<30 GeV in the most forward region 3.2≤|η|<4.5. The jet energy is validated for jet transverse momenta up to 1 TeV to the level of a few percent using several in situ techniques by comparing a well-known reference such as the recoiling photon pT, the sum of the transverse momenta of tracks associated to the jet, or a system of low-pT jets recoiling against a high-pT jet. More sophisticated jet calibration schemes are presented based on calorimeter cell energy density weighting or hadronic properties of jets, aiming for an improved jet energy resolution and a reduced flavour dependence of the jet response. The systematic uncertainty of the jet energy determined from a combination of in situ techniques is consistent with the one derived from single hadron response measurements over a wide kinematic range. The nominal corrections and uncertainties are derived for isolated jets in an inclusive sample of high-pT jets. Special cases such as event topologies with close-by jets, or selections of samples with an enhanced content of jets originating from light quarks, heavy quarks or gluons are also discussed and the corresponding uncertainties are determined.

1 Introduction

Collimated sprays of energetic hadrons, called jets, are the dominant feature of high energy hard proton-proton interactions at the Large Hadron Collider (LHC) at CERN. In Quantum Chromodynamics (QCD) jets are produced via the fragmentation of quarks and gluons. They are key ingredients for many physics measurements and for searches for new phenomena.

During the year 2010 the ATLAS detector collected proton-proton collision data at a centre-of-mass energy of \(\sqrt{s}=7~ \mathrm{TeV} \) corresponding to an integrated luminosity of 38 pb−1. The uncertainty in the jet energy measurement is the dominant experimental uncertainty for numerous physics results, for example the cross-section measurement of inclusive jets, dijets or multijets [1, 2, 3, 4, 5], as well as of vector bosons accompanied by jets [6, 7], and new physics searches with jets in the final state [8, 9, 10, 11, 12, 13]. The energy measurement of jets produced in proton-proton and electron-proton collisions was also discussed by previous experiments [14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24].

Jets are observed as groups of topologically related energy deposits in the ATLAS calorimeters. The anti-kt jet algorithm [25] is adopted as the standard way to reconstruct jets.

Using a Monte Carlo (MC) simulation the observed jets are calibrated such that, on average, the jet energy corresponds to that of the associated stable particles in the ATLAS detector. The calibration of the jet energy scale (JES) should ensure the correct measurement of the average energy across the whole detector and needs to be independent of proton-proton collision events produced in addition to the event of interest.

In this document, the jet calibration strategies adopted by the ATLAS experiment are outlined and studies to evaluate the uncertainties in the jet energy measurement are presented. A first estimate of the JES uncertainty, described in Ref. [1], was based on information available before the first LHC collisions. It also exploited transverse momentum balance in events with only two jets at high transverse momenta (pT). A reduced uncertainty with respect to Ref. [1] is presented that is based on the increased knowledge of the detector performance obtained during the analysis of the first year of ATLAS data taking.

ATLAS has developed several jet calibration schemes [26] with different levels of complexity and different sensitivity to systematic effects, which are complementary in their contribution to the jet energy measurement. Each calibration scheme starts from the measured calorimeter energy at the electromagnetic (EM) energy scale, which correctly measures the energy deposited by electromagnetic showers. In the simplest case, called EM+JES calibration scheme, the jet energy is measured on EM scale and the jet calibration is derived as a simple correction relating the calorimeter’s response to the true jet energy. More sophisticated schemes exploit the topology of the calorimeter energy depositions to correct for calorimeter non-compensation (nuclear energy losses, etc.) and other jet reconstruction effects. For the EM+JES calibration scheme the JES uncertainty can be determined from the single hadron response measurements in small data sets collected in situ or in test-beams. With a large data set available the JES uncertainty can also be determined using the ratio of the jet transverse momentum to the momentum of a well measured reference object and by a comparison of the data to the Monte Carlo simulation.

Several techniques have been developed to directly determine the uncertainty on the jet energy measurement in situ. The JES uncertainty can be obtained by comparing the jet energy to a well calibrated reference object. A standard technique to probe the absolute jet energy scale, used also in earlier hadron collider experiments, is to measure the pT balance between the jet and a well-measured object: a photon or a Z boson. However, the currently limited data statistics imposes a limit on the pT range that can be tested with this technique. The JES uncertainty on higher jet transverse momenta up to the TeV-scale can be assessed using the multijet balance technique where a recoil system of well-calibrated jets at lower pT is balanced against a single jet at higher pT. A complementary technique uses the total momentum of the tracks associated to the jets as reference objects. While the resolution of the jet energy measurement using tracks in jets is rather poor, the mean jet energy can be determined to the precision of a few percent.

The standard jet calibration and the corresponding uncertainty on the energy measurement are determined for isolated jets in an inclusive jet data sample. Additional uncertainties are evaluated for the dependence of the calorimeter response to details of jet fragmentation like differences between jets induced by quarks or gluons. Also special event topologies with close-by jets are investigated.

The outline of the paper is as follows. First the ATLAS detector (Sect. 2) is described. An overview of the jet calibration procedures and the various calibration schemes is given in Sect. 3. The Monte Carlo simulation framework is introduced in Sect. 4. The data samples, data quality assessment and event selection are described in Sect. 5. Then, the reconstruction (Sect. 6), and the selection (Sect. 7) of jets are discussed. The jet calibration method is outlined in Sect. 8 which includes a prescription to correct for the extra energy due to multiple proton-proton interactions (pile-up).

Section 9 describes the sources of systematic uncertainties for the jet energy measurement and their estimation using Monte Carlo simulations and collision data. Section 10 describes several in situ techniques used to validate these systematic uncertainties. Section 11 presents a technique to improve the resolution of the energy measurements and to reduce the flavour response differences by exploiting the topology of the jets. The systematic uncertainties associated with this technique are described in Sect. 12. The jet calibration schemes based on calorimeter cell energy weighting in jets are introduced in Sect. 13, and the associated JES uncertainties are estimated from the in situ techniques as described in Sect. 14. Section 15 summarises the systematic uncertainties for all studied jet calibration schemes.

The jet reconstruction efficiency and its uncertainty is discussed in Sect. 16. The response uncertainty of non-isolated jets is investigated in Sect. 17, while Sects. 18 and 19 discuss response difference for jets originating from light quarks or gluons and presents a method to determine, on average, the jet flavour content in a given data sample. In Sect. 20 JES uncertainties for jets where a heavy quark is identified are investigated. Finally, possible effects from lack of full calorimeter containment of jets with high transverse momentum are studied in Sect. 21. The overall conclusion is given in Sect. 22.

The present paper discusses the precision of the mean jet energy measurement. The jet energy resolution [27] and calorimeter response uncertainty from single hadron response measurements [28] are discussed elsewhere.

2 The ATLAS detector

The ATLAS detector is a multi-purpose detector designed to observe particles produced in proton-proton and heavy ion collisions. A detailed description can be found in Ref. [29]. The detector consists of an inner detector, sampling electromagnetic and hadronic calorimeters and muon chambers. Figure 1 shows a sketch of the detector outline together with an event with two jets at high transverse momenta.
Fig. 1

Display of the central part of the ATLAS detector in the x-z view showing the highest mass central dijet event collected during the 2010 data taking period. The two leading jets have \(p_{\mathrm{T}} ^{\mathrm{jet}} = 1.3~ \mathrm{TeV} \) with y=−0.68 and \(p_{\mathrm{T}} ^{\mathrm{jet}} = 1.2~ \mathrm{TeV} \) with y=0.64, respectively. The two leading jets have an invariant mass of approximately 3.1 TeV. The missing transverse energy in the event is 46 GeV. The lines in the inner detector indicate the reconstructed particle trajectories. The energy deposition in the calorimeter cells are displayed as light rectangles. The area of the rectangles is proportional to the energy deposits. The histograms attached to the LAr and the Tile calorimeter illustrate the amount of deposited energy

The inner detector (ID) is a tracking system immersed in a magnetic field of 2 T provided by a solenoid and covers a pseudorapidity1 |η|≲2.5. The ID barrel region |η|≲2 consists of three layers of pixel detectors (Pixel) close to the beam-pipe, four layers of double-sided silicon micro-strip detectors (SCT) providing eight hits per track at intermediate radii, and a transition radiation tracker (TRT) composed of straw tubes in the outer part providing 35 hits per track. At |η|>1 the ID endcap regions each provide three Pixel discs and nine SCT discs perpendicular to the beam direction.

The liquid argon (LAr) calorimeter is composed of sampling detectors with full azimuthal symmetry, housed in one barrel and two endcap cryostats. A highly granular electromagnetic (EM) calorimeter with accordion-shaped electrodes and lead absorbers in liquid argon covers the pseudorapidity range |η|<3.2. It contains a barrel part (EMB, |η|<1.475) and an endcap part (EMEC, 1.375≤|η|<3.2) each with three layers in depth (from innermost to outermost EMB1, EMB2, EMB3 and EMEC1, EMEC 2, EMEC3). The middle layer has a 0.025×0.025 granularity in η×ϕ space. In total, the EM calorimeter has a thickness of X0=22 (X0=24) radiation lengths. The innermost layer (strips) consists of cells with eight times finer granularity in the η-direction and with 3-times coarser granularity in the ϕ direction.

For |η|<1.8, a presampler (PS), consisting of an active LAr layer is installed directly in front of the EM calorimeters, and provides a measurement of the energy lost before the calorimeter.

A copper-liquid argon hadronic endcap calorimeter (HEC, 1.5≤|η|<3.2) is located behind the EMEC. A copper/tungsten-liquid argon forward calorimeter (FCal) covers the region closest to the beam at 3.1≤|η|<4.9. The HEC has four layers and the FCAL has three layers. From innermost to outermost these are: HEC0, HEC1, HEC2, HEC3 and FCal0, FCal1, FCal2. Altogether, the LAr calorimeters correspond to a total of 182,468 readout cells, i.e. 97.2 % of the full ATLAS calorimeter readout.

The hadronic Tile calorimeter (|η|<1.7) surrounding the LAr cryostats completes the ATLAS calorimetry. It consists of plastic scintillator tiles and steel absorbers covering |η|<0.8 for the barrel and 0.8≤|η|<1.7 for the extended barrel. Radially, the hadronic Tile calorimeter is segmented into three layers, approximately 1.4, 3.9 and 1.8 interaction lengths thick at η=0; the Δη×Δϕ segmentation is 0.1×0.1 (0.2×0.1 in the last radial layer). The last layer is used to catch the tails of the longitudinal shower development. The three radial layers of the Tile calorimeter will be referred to (from innermost to outermost) as Tile0, Tile1, Tile2.2

The ATLAS calorimeter covers a total thickness of λ=11 interaction lengths at η=0.

Between the barrel and the extended barrels there is a gap of about 60 cm, which is needed for the ID and the LAr services. Gap scintillators (Gap) covering the region 1.0≤|η|<1.2 are installed on the inner radial surface of the extended barrel modules in the region between the Tile barrel and the extended barrel. Crack scintillators (Scint) are located on the front of the LAr endcap and cover the region 1.2≤|η|<1.6.

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 |η|<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 higher level trigger (HLT) [30]. Jets are first identified at L1 using a sliding window algorithm from coarse granularity calorimeter towers. This is refined using jets reconstructed from calorimeter cells in the HLT. The lowest threshold inclusive jet trigger is fully efficient for jets with pT≳60 GeV. Events with lower pT jets are triggered by the minimum bias trigger scintillators (MBTS) mounted at each end of the detector in front of the LAr endcap calorimeter cryostats at |z|=±3.56 m.

3 Introduction to jet energy calibration methods

Hadronic jets used for ATLAS physics analyses are reconstructed by a jet algorithm starting from the energy depositions of electromagnetic and hadronic showers in the calorimeters. An example of a jet recorded by the ATLAS detector and displayed in the plane transverse to the beam line is shown in Fig. 2.
Fig. 2

Zoom of the x-y view of the ATLAS detector showing one of the high-pT jets of the event shown in Fig. 1. The energy depositions in the calorimeter cells are displayed as light rectangles. The area of the rectangles is proportional to the energy deposits. The dark histograms attached to the LAr (Tile) calorimeter illustrates the amount of deposited energy. The lines in the ID display the reconstructed tracks originating from the interaction vertex

The jet Lorentz four-momentum is reconstructed from the corrected energy and angles with respect to the primary event vertex. For systematic studies and calibration purposes track jets are built from charged particles using their momenta measured in the inner detector. Reference jets in Monte Carlo simulations (truth jets) are formed from simulated stable particles using the same jet algorithm (without detector simulation). The jet energy calibration relates the jet energy measured with the ATLAS calorimeter to the true energy of the corresponding truth jet entering the ATLAS detector.

The jet calibration corrects for the following detector effects that affect the jet energy measurement:
  1. 1.

    Calorimeter non-compensation: partial measurement of the energy deposited by hadrons.

     
  2. 2.

    Dead material: energy losses in inactive regions of the detector.

     
  3. 3.

    Leakage: energy of particles reaching outside the calorimeters.

     
  4. 4.

    Out of calorimeter jet cone: energy deposits of particles inside the truth jet entering the detector that are not included in the reconstructed jet.

     
  5. 5.

    Noise thresholds and particle reconstruction efficiency: signal losses in the calorimeter clustering and jet reconstruction.

     

Jets reconstructed in the calorimeter system are formed from calorimeter energy depositions reconstructed at the electromagnetic energy scale (EM) or from energy depositions that are corrected for the lower detector response to hadrons. The EM scale correctly reconstructs the energy deposited by particles in an electromagnetic shower in the calorimeter. This energy scale is established using test-beam measurements for electrons in the barrel [31, 32, 33, 34, 35], the endcap [36, 37] and the FCAL [38, 39] calorimeters. The absolute calorimeter response to energy deposited via electromagnetic processes was validated in the hadronic calorimeters using muons, both from test-beams [35, 40] and produced in situ by cosmic rays [41]. The energy scale of the electromagnetic calorimeters is corrected using the invariant mass of Z bosons produced in proton-proton collisions (Ze+e events) [42]. The correction for the lower response to hadrons is solely based on the topology of the energy depositions observed in the calorimeter.

In the simplest case, called EM+JES calibration scheme, the jet energy is measured on EM scale and the jet calibration is derived as a simple correction relating the calorimeter response to the true jet energy, as follows: The variable \(E^{\mathrm{jet}}_{ \mathrm{EM} }\) is the calorimeter energy measured at the electromagnetic scale, \(E^{\rm jet}_{\rm calib}\) is the calibrated jet energy and \(\mathcal{F}_{{\rm calib}}\) is the calibration function that depends on the measured jet energy and is evaluated in small jet pseudorapidity regions. The variable \(\mathcal{O}( N_{\mathrm{PV}} )\) denotes the correction for additional energy from multiple proton-proton interactions depending on the number of primary vertices (NPV).

The simplest calibration scheme applies the JES corrections to jets reconstructed at the electromagnetic scale. This calibration scheme allows a simple evaluation of the systematic uncertainty from single hadron response measurements and systematic Monte Carlo variations. This can be achieved with small data sets and is therefore suitable for early physics analyses.

Other calibration schemes use additional cluster-by-cluster and/or jet-by-jet information to reduce some of the sources of fluctuations in the jet energy response, thereby improving the jet energy resolution [26, 27]. For these calibration schemes the same jet calibration procedure is applied as for the EM+JES calibration scheme, but the energy corrections are numerically smaller.

The global calorimeter cell weighting (GCW) calibration exploits the observation that electromagnetic showers in the calorimeter leave more compact energy depositions than hadronic showers with the same energy. Energy corrections are derived for each calorimeter cell within a jet, with the constraint that the jet energy resolution is minimised. These cell corrections account for all energy losses of a jet in the ATLAS detector. Since these corrections are only applicable to jets and not to energy depositions in general, they are called “global” corrections.

The local cluster weighting (LCW) calibration method first clusters together topologically connected calorimeter cells and classifies these clusters as either electromagnetic or hadronic. Based on this classification energy corrections are derived from single pion Monte Carlo simulations. Dedicated corrections are derived for the effects of non-compensation, signal losses due to noise threshold effects, and energy lost in non-instrumented regions. They are applied to calorimeter clusters and are defined without reference to a jet definition. They are therefore called “local” corrections. Jets are then built from these calibrated clusters using a jet algorithm.

The final jet energy calibration (see Eq. (1)) can be applied to EM scale jets, with the resulting calibrated jets referred to as EM+JES, or to GCW and LCW calibrated jets, with the resulting jets referred to as GCW+JES and LCW+JES jets. The jet energy scale (JES) is different for each calibration scheme.

A further jet calibration scheme, called global sequential (GS) calibration, starts from jets calibrated with the EM+JES calibration and exploits the topology of the energy deposits in the calorimeter to characterise fluctuations in the jet particle content of the hadronic shower development. Correcting for such fluctuations can improve the jet energy resolution [27]. The corrections are applied such that the mean jet energy in the inclusive case is left unchanged. The correction uses several jet properties and each correction is applied sequentially. In particular, the longitudinal and transverse structure of the hadronic shower in the calorimeter is exploited.

The simple EM+JES jet calibration scheme does not provide the best performance, but allows in the central detector region the most direct evaluation of the systematic uncertainties from the calorimeter response to single isolated hadron measured in situ and in test-beams and from systematic variations of the Monte Carlo simulation. For the GS calibration scheme the systematic uncertainty is obtained by studying the response after applying the GS calibration with respect to the EM+JES calibration. For the GCW+JES and LCW+JES calibration schemes the JES uncertainty is determined from in situ techniques.

For all calibration schemes the JES uncertainty in the forward detector regions is derived from the uncertainty in the central region using the transverse momentum balance in events where only two jets are produced.

In the following, the calibrated calorimeter jet transverse momentum will be denoted as \(p_{\mathrm{T}} ^{\mathrm{jet}}\), and the jet pseudorapidity as η.

4 Monte Carlo simulation

4.1 Event generators

The energy and direction of particles produced in proton-proton collisions are simulated using various event generators. An overview of Monte Carlo event generators for LHC physics can be found in Ref. [43]. The samples using different event generators and theoretical models used are described below:
  1. 1.

    Pythia with the MC10 or AMBT1 tune: The event generator Pythia [44] simulates non-diffractive proton-proton collisions using a 2→2 matrix element at leading order in the strong coupling to model the hard subprocess, and uses pT-ordered parton showers to model additional radiation in the leading-logarithmic approximation [45]. Multiple parton interactions [46], as well as fragmentation and hadronisation based on the Lund string model [47] are also simulated. The proton parton distribution function (PDF) set used is the modified leading-order PDF set MRST LO* [48]. The parameters used for tuning multiple parton interactions include charged particle spectra measured by ATLAS in minimum bias collisions [49], and are denoted as the ATLAS MC10 tune [50].

     
  2. 2.

    The Perugia2010 tune is an independent tune of Pythia with increased final state radiation to better reproduce the jet shapes and hadronic event shapes using LEP and Tevatron data [51]. In addition, parameters sensitive to the production of particles with strangeness and related to jet fragmentation have been adjusted.

     
  3. 3.

    Herwig+Jimmy uses a leading order 2→2 matrix element supplemented with angular-ordered parton showers in the leading-logarithm approximation [52, 53, 54]. The cluster model is used for the hadronisation [55]. Multiple parton interactions are modelled using Jimmy [56]. The model parameters of Herwig/Jimmy have been tuned to ATLAS data (AUET1 tune) [57]. The MRST LO* PDF set [48] is used.

     
  4. 4.

    Herwig++ [58] is based on the event generator Herwig, but redesigned in the C++ programming language. The generator contains a few modelling improvements. It also uses angular-ordered parton showers, but with an updated evolution variable and a better phase space treatment. Hadronisation is performed using the cluster model. The underlying event and soft inclusive interactions are described using a hard and soft multiple partonic interactions model [59]. The MRST LO* PDF set [48] is used.

     
  5. 5.

    Alpgen is a tree level matrix-element generator for hard multi-parton processes (2→n) in hadronic collisions [60]. It is interfaced to Herwig to produce parton showers in the leading-logarithmic approximation. Parton showers are matched to the matrix element with the MLM matching scheme [61]. For the hadronisation, Herwig is used and soft multiple parton interactions are modelled using Jimmy[56] (with the ATLAS MC09 tune [62]). The PDF set used is CTEQ6L1 [63].

     

4.2 Simulation of the ATLAS detector

The Geant4 software toolkit [64] within the ATLAS simulation framework [65] propagates the generated particles through the ATLAS detector and simulates their interactions with the detector material. The energy deposited by particles in the active detector material is converted into detector signals with the same format as the ATLAS detector read-out. The simulated detector signals are in turn reconstructed with the same reconstruction software as used for the data.

In Geant4 the model for the interaction of hadrons with the detector material can be specified for various particle types and for various energy ranges. For the simulation of hadronic interactions in the detector, the Geant4 set of processes called QGSP_BERT is chosen [66]. In this set of processes, the Quark Gluon String model [67, 68, 69, 70, 71] is used for the fragmentation of the nucleus, and the Bertini cascade model [72, 73, 74, 75] for the description of the interactions of hadrons in the nuclear medium.

The Geant4 simulation and in particular the hadronic interaction model for pions and protons, has been validated with test-beam measurements for the barrel [35, 76, 77, 78, 79] and endcap [36, 37, 80] calorimeters. Agreement within a few percent is found between simulation and data of the average calorimeter response to pions with momenta between 2 GeV and 350 GeV.

Further tests have been carried out in situ comparing the single hadron response, measured using isolated tracks and identified single particles. Agreement within a few percent is found for the inclusive measurement and for identified pions and protons from the decay products of kaon and lambda particles produced in proton-proton collisions at 7 TeV [28]. With this method particle momenta of pions and protons in the range from a few hundred MeV to 6 GeV can be reached. Good agreement between Monte Carlo simulation and data is found.

4.3 Nominal Monte Carlo simulation samples

The baseline (nominal) Monte Carlo sample used to derive the jet energy scale and to estimate the sources of its systematic uncertainty is a sample containing high-pT jets produced via strong interactions. It is generated with the Pythia event generator with the MC10 tune (see Sect. 4.1), passed through the full ATLAS detector simulation and is reconstructed as the data.

The ATLAS detector geometry used in the simulation of the nominal sample reflects the geometry of the detector as best known at the time of these studies. Studies of the material of the inner detector in front of the calorimeters have been performed using secondary hadronic interactions [81]. Additional information is obtained from studying photon conversions [82] and the energy flow in minimum bias events [83].

4.4 Simulated pile-up samples

For the study of multiple proton-proton interactions, two samples have been used, one for in-time and one for out-of-time pile-up. The first simulates additional proton-proton interactions per bunch crossing, while the second one also contains pile-up arising from bunches before or after the bunch where the event of interest was triggered (for more details see Sect. 5 and Sect. 8.1). The bunch configuration of LHC (organised in bunch trains) is also simulated. The additional number of primary vertices in the in-time (bunch-train) pile-up sample is 1.7 (1.9) on average.

5 Data sample and event selection

5.1 Data taking period and LHC conditions

Proton-proton collisions at a centre-of-mass energy of \(\sqrt{s} = 7~ \mathrm{TeV} \), recorded from March to October 2010 are analysed. Only data with a fully functioning calorimeter and inner detector are used. The data set corresponds to an integrated luminosity of 38 pb−1. Due to different data quality requirements the integrated luminosity can differ for the various selections used in the in situ technique analyses.

Several distinct periods of machine configuration and detector operation were present during the 2010 data taking. As the LHC commissioning progressed, changes in the beam optics and proton bunch parameters resulted in changes in the number of pile-up interactions per bunch crossing. The spacing between the bunches was no less than 150 ns.

Figure 3 shows the evolution of the maximum of the distribution of the number of interactions (peak) derived from the online luminosity measurement and assuming an inelastic proton-proton scattering cross section of 71.5 mb [84].
Fig. 3

The peak number of interactions per bunch crossing (“BX”) as measured online by the ATLAS luminosity detectors [84]

The very first data were essentially devoid of multiple proton-proton interactions until the optics of the accelerator beam (specifically β) were changed in order to decrease the transverse size of the beam and increase the luminosity.3 This change alone raised the fraction of events with at least two observed interactions from less than 2 % to between 8 % and 10 % (May–June 2010).

A further increase in the number of interactions occurred when the number of protons per bunch (ppb) was increased from approximately 5–9⋅1010 to 1.15⋅1011 ppb. Since the number of proton-proton collisions per bunch crossing is proportional to the square of the bunch intensity, the fraction of events with pile-up increased to more than 50 % for runs between June and September 2010.

Finally, further increasing the beam intensity slowly raised the average number of interactions per bunch crossing to more than three by the end of the proton-proton run in November 2010.

5.2 Event selection

Different triggers are used to select the data samples, in order to be maximally efficient over the entire jet pT-range of interest. The dijet sample is selected using the hardware-based calorimeter jet triggers [30, 85], which are fully efficient for jets with \(p_{\mathrm{T}} ^{\mathrm{jet}} > 60~ \mathrm{GeV} \). For lower \(p_{\mathrm{T}} ^{\mathrm{jet}}\) a trigger based on the minimum bias trigger scintillators is used.

The multijet sample uses either the inclusive jet trigger or a trigger that requires at least two, three or more jets with pT>10 GeV at the EM scale. These triggers are fully efficient for jets with \(p_{\mathrm{T}} ^{\mathrm{jet}} > 80~ \mathrm{GeV} \).

Each event is required to have a primary hard scattering vertex. A primary vertex is required to have at least five tracks \((N_{\rm pp}^{\rm tracks})\) with a transverse momentum of \(p_{\mathrm{T}} ^{\mathrm{track}} > 150~ \mathrm{MeV} \). The primary vertex associated to the event of interest (hard scattering vertex) is the one with the highest associated transverse track momentum squared, \(\sum( p_{\mathrm{T}} ^{\rm track})^{2}\), used in the vertex fit where the sum runs over all tracks used in the vertex fit. This renders the contribution from fake vertices due to beam backgrounds to be negligible.

The γ-jet sample is selected using a photon trigger [30] that is fully efficient for photons passing offline selections. The higher threshold for the photon pT is 40 GeV and this trigger was not pre-scaled; the lower threshold is 20 GeV and this trigger was pre-scaled at high luminosity.

5.3 Data quality assessment

The ATLAS data quality (DQ) selection is based upon inspection of a standard set of distributions that leads to a data quality assessment for each subdetector, usually segmented into barrel, forward and endcap regions, as well as for the trigger and for each type of reconstructed physics object (jets, electrons, muons, etc.). Each subsystem sets its own DQ flags, which are recorded in a conditions database. Each analysis applies DQ selection criteria, and defines a set of luminosity blocks (each corresponds to approximately two minutes of data taking). The good luminosity blocks used are those not flagged for having issues affecting a relevant subdetector.

Events with minimum bias and calorimeter triggers were required to belong to specific runs and run periods in which the detector, trigger and reconstructed physics objects have passed a data quality assessment and are deemed suitable for physics analysis.

The primary systems of interest for this study are the electromagnetic and hadronic calorimeters, and the inner tracking detector for studies of the properties of tracks associated with jets.

6 Jet reconstruction

In data and Monte Carlo simulation jets are reconstructed using the anti-kt algorithm [25] with distance parameters R=0.4 or R=0.6 using the FastJet software [86, 87]. The four-momentum recombination scheme is used. For jet finding rapidity y is used, while jet corrections and performance studies use often pseudorapidity η. The jet pT reconstruction threshold is \(p_{\mathrm{T}} ^{\mathrm{jet}} > 7~ \mathrm{GeV} \).

In the following, only anti-kt jets with distance parameter R=0.6 are discussed in detail. The results for jets with R=0.4 are similar, if not stated otherwise.

6.1 Reconstructed calorimeter jets

The input to calorimeter jets can be topological calorimeter clusters (topo-clusters) [37, 88] or calorimeter towers. Only topo-clusters or towers with a positive energy are considered as input to jet finding.

6.1.1 Topological calorimeter clusters

Topological clusters are groups of calorimeter cells that are designed to follow the shower development taking advantage of the fine segmentation of the ATLAS calorimeters. The topo-cluster formation algorithm starts from a seed cell, whose signal-to-noise (S/N) ratio (estimated as the absolute value of the energy deposited in the calorimeter cell over the RMS of the energy distribution measured in randomly triggered events without proton-proton collisions) is above a threshold of S/N=4. Cells neighbouring the seed (or the cluster being formed) that have a signal-to-noise ratio of at least S/N=2 are included iteratively. Finally, all calorimeter cells neighbouring the formed topo-cluster are added. The topo-cluster algorithm efficiently suppresses the calorimeter noise.

The topo-cluster algorithm also includes a splitting step in order to optimise the separation of showers from different close-by particles: All cells in a topo-cluster are searched for local maxima in terms of energy content with a threshold of 500 MeV. This means that the selected calorimeter cell has to be more energetic than any of its neighbours. The local maxima are then used as seeds for a new iteration of topological clustering, which splits the original cluster into more topo-clusters.

A topo-cluster is defined to have an energy equal to the energy sum of all the included calorimeter cells, zero mass and a reconstructed direction calculated from the weighted averages of the pseudorapidities and azimuthal angles of the constituent cells. The weight used is the absolute cell energy and the positions of the cells are relative to the nominal ATLAS coordinate system.

6.1.2 Calorimeter towers

Calorimeter towers are static, Δη×Δϕ=0.1×0.1, grid elements built directly from calorimeter cells.4

ATLAS uses two types of calorimeter towers: with and without noise suppression. Calorimeter towers based on all calorimeter cells are called non-noise-suppressed calorimeter towers in the following. Noise-suppressed towers make use of the topo-clusters algorithm, i.e. only calorimeter cells that are included in topo-clusters are used. Therefore, in a fixed geometrical area the same calorimeter cells are used for noise-suppressed towers and topo-clusters.

Both types of calorimeter towers have an energy equal to the energy sum of all included calorimeter cells. The formed Lorentz four-momentum has zero mass.

6.2 Reconstructed track jets

Jets built from charged particle tracks originating from the primary hard scattering vertex (track jets) are used to define jets that are insensitive to the effects of pile-up and provide a stable reference to study close-by jet effects.

Tracks with \(p_{\mathrm{T}} ^{\rm track}>0.5~ \mathrm{GeV} {}\) and |η|<2.5 are selected. They are required to have at least one (six) hit(s) in the Pixel (SCT) detector. The transverse (d0) and longitudinal (z0) impact parameters of the tracks measured with respect to the primary vertex are also required to be |d0|<1.5 mm and |z0sinθ|<1.5 mm, respectively.

The track jets must have at least two constituent tracks and a total transverse momentum of \(p^{\text{track jet}}_{\mathrm{T}} >3~ \mathrm{GeV} \). Since the tracking system has a coverage up to |η|=2.5, the performance studies of calorimeter jets is carried out in the range |η|<1.9 for R=0.6 and |η|<2.1 for R=0.4.

6.3 Monte Carlo truth jets and flavour association

In the Monte Carlo simulation truth jets are defined from stable particles defined to have proper lifetimes longer than 10 ps excluding muons and neutrinos.

For certain studies, jets in the Monte Carlo simulation are additionally identified as jets initiated by light or heavy quarks or by gluons based on the generator event record. The highest energy parton that points to the truth jet5 determines the flavour of the jet. Using this method, only a small fraction of the jets (<1 % at low pT and less at high pT) could not be assigned a partonic flavour.6 This definition is sufficient to study the flavour dependence of the jet response. Any theoretical ambiguities of jet flavour assignment do not need to be addressed in the context of a performance study.

7 Jet quality

Jets at high transverse momenta produced in proton-proton collisions must be distinguished from background jets not originating from hard scattering events. The main backgrounds are the following:
  1. 1.

    Beam-gas events, where one proton of the beam collided 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.

    Large calorimeter noise.

     

The criteria to efficiently reject jets arising from background are only applied to data. They are discussed in the following sections.

7.1 Criteria to remove non-collision background

7.1.1 Noise in the calorimeters

Two types of calorimeter noise are addressed:
  1. 1.

    Sporadic noise bursts in the hadronic endcap calorimeter (HEC), where a single noisy calorimeter cell contributes almost all of the jet energy. Jets reconstructed from these problematic cells are characterised by a large energy fraction in the HEC calorimeter (fHEC) as well as a large fraction of the energy in calorimeter cells with poor signal shape quality7 (fHECquality). Due to the capacitive coupling between channels, the neighbouring calorimeter cells with little genuine energy will have an apparent negative energy (\(E_{\rm neg}\)).

     
  2. 2.

    Rare coherent noise in the electromagnetic calorimeter. Similarly, fake jets arising from this source are characterised by a large electromagnetic energy fraction (\(f_{\rm EM}\)),8 and a large fraction of energy in EM calorimeter cells with poor signal shape quality (\(f_{\rm quality}\)).

     

7.1.2 Cosmic rays or non-collision background

Cosmic rays or non-collision backgrounds can induce events where the jet candidates are not in-time with the beam collision. A cut on the jet time (\(t_{\rm jet}\)) is applied to reject these backgrounds. The jet time is reconstructed from the energy deposition in the calorimeter by weighting the reconstructed time of calorimeter cells forming the jet with the square of the cell energy. The calorimeter time is defined with respect to the event time recorded by the trigger.

A cut on the \(f_{\rm EM}\) is applied to make sure that the jet has some energy deposited in the calorimeter layer closest to the interaction region as expected for a jet originating from the nominal interaction point.

Since a real jet is expected to have tracks, the fEM cut is applied together with a cut on the minimal jet charged fraction (\(f_{\rm ch}\)), defined as the ratio of the scalar sum of the pT of the tracks associated to the jet divided by the jet pT, for jets within the tracking acceptance.

A cut on the maximum energy fraction in any single calorimeter layer (\(f_{\rm max}\)) is applied to further reject non-collision background.

7.1.3 Jet quality selections

Two quality selections are provided:
  1. 1.

    A loose selection is designed with an efficiency above 99 %, that can be used in most of the ATLAS physics analyses.

     
  2. 2.

    A medium selection is designed for analyses that select jets at high transverse momentum, such as for jet cross-section measurements [1].

     
A tight quality selection has been developed for the measurement of the jet quality selection efficiency described in Sect. 7.2, but is not used in physics analyses, since the medium jet quality selection is sufficient for removing fake jets. The quality selection criteria used to identify and reject fake jets are listed in Table 1.
Table 1

Selection criteria used to reject fake jets and non-collision background

 

Loose

Medium

HEC spikes

(fHEC>0.5 and |fHECquality|>0.5) or \(|E_{\rm neg}| > 60~ \mathrm{GeV} \)

Loose or fHEC>1−|fHECquality|

Coherent EM noise

fEM>0.95 and \(f_{{\rm quality}} > 0.8\) and |η|<2.8

Loose or fEM>0.9 and \(f_{\rm quality}>0.8\) and |η|<2.8

Non-collision background

\(|t_{\rm jet} | > 25~\mathrm{ns}\) or (fEM<0.05 and \(f_{\rm ch} < 0.05\) and |η|<2) or (fEM<0.05 and |η|≥2) or (\(f_{\rm max} > 0.99\) and |η|<2)

Loose or \(|t_{\rm jet}| > 10\mbox{~ns}\) or (fEM<0.05 and \(f_{\rm ch} < 0.1\) and |η|<2) or (fEM>0.95 and \(f_{\rm ch} < 0.05\) and |η|<2)

7.2 Evaluation of the jet quality selection efficiency

The criteria for the jet quality selection are optimised by studying samples with good and fake jets classified by their amount of missing transverse momentum significance:9
  1. 1.

    Good jets belong to events where the two leading jets have \(p_{\mathrm{T}} ^{\mathrm{jet}} > 20~ \mathrm{GeV} \), and are back-to-back (Δϕj−j>2.6 radian) in the plane transverse to the beam, and with a small missing transverse momentum significance \({E}_{\mathrm{T}}^{\mathrm{miss}} /\sqrt{\sum E_{\rm T}} < 1\).

     
  2. 2.

    Fake jets belong to events with a high transverse momentum significance \({E}_{\mathrm{T}}^{\mathrm{miss}} /\sqrt{\sum E_{\rm T}} > 3\) and with a reconstructed jet back-to-back to the missing transverse momentum direction (\(\Delta\phi_{ {E}_{\mathrm{T}}^{\mathrm{miss}} -{\rm j}} > 2.6\mbox{~radian}\)).

     
The good jets sample is used to study the jet selection inefficiency and the bad jet sample is used to study the rejection power.
As the jet quality selection criteria are only applied to data an efficiency correction for data is determined. This efficiency is measured using a tag-and-probe method in events with two jets at high transverse momentum. The reference jet (\(p_{\mathrm{T}} ^{\rm ref}\)) is required to pass the tightened version of the jet quality selections, and to be back-to-back (\(\Delta\phi_{ {E}_{\mathrm{T}}^{\mathrm{miss}} -{\rm j}} > 2.6\mbox{~radian}\)) and well-balanced with the probe jet (\(p_{\mathrm{T}} ^{\rm probe}\)): The jet quality selection criteria were then applied to the probe jets, measuring the fraction of jets passing as a function of η and \(p_{\mathrm{T}} ^{\mathrm{jet}} \).
The resulting efficiencies for jets with R=0.6 for loose and medium selections applied to the probe jets are shown in Fig. 4. The tight selection of the reference jet was varied to study the systematic uncertainty. The loose selection criteria are close to 100 % efficient. In the forward region the medium selection criteria are also close to fully efficient. In the central region they have an efficiency of 99 % for \(p_{\mathrm{T}} ^{\mathrm{jet}} > 50~ \mathrm{GeV} \). For lower pT jets of about 25 GeV an inefficiency of up to 3–4 % is observed.
Fig. 4

Jet quality selection efficiency for anti-kt jets with R=0.6 measured with a tag-and-probe technique as a function of \(p_{\mathrm{T}} ^{\mathrm{jet}}\) in bins of η, for loose and medium selection criteria (see Table 1). Only statistical uncertainties are shown. In (e), (f), (g) the loose and medium results overlap

7.3 Summary of the jet quality selection

Quality selections used to reject fake jets with the ATLAS detector have been developed. Simple variables allow the removal of fake jets due to sporadic noise in the calorimeter or non-collision background at the analysis level, with an efficiency greater than 99 % over a wide kinematic range.

8 Jet energy calibration in the EM+JES scheme

The simple EM+JES calibration scheme applies corrections as a function of the jet energy and pseudorapidity to jets reconstructed at the electromagnetic scale.

The additional energy due to multiple proton-proton collisions within the same bunch crossing (pile-up) is corrected before the hadronic energy scale is restored, such that the derivation of the jet energy scale calibration is factorised and does not depend on the number of additional interactions measured.

The EM+JES calibration scheme consists of three subsequent steps as outlined below and detailed in the following subsections:
  1. 1.

    Pile-up correction: The average additional energy due to additional proton-proton interactions is subtracted from the energy measured in the calorimeters using correction constants obtained from in situ measurements.

     
  2. 2.

    Vertex correction: The direction of the jet is corrected such that the jet originates from the primary vertex of the interaction instead of the geometrical centre of the detector.

     
  3. 3.

    Jet energy and direction correction: The jet energy and direction as reconstructed in the calorimeters are corrected using constants derived from the comparison of the kinematic observables of reconstructed jets and those from truth jets in Monte Carlo simulation.

     

8.1 Pile-up correction

8.1.1 Correction strategy

The measured energy of reconstructed jets can be affected by contributions that do not originate from the hard scattering event of interest, but are instead produced by additional proton-proton collisions. An offset correction for pile-up is derived from minimum bias data as a function of the number of reconstructed primary vertices, NPV, the jet pseudorapidity, η, and the bunch spacing.

This offset correction applied to the jet transverse energy (ET) at the EM scale as the first step of jet calibration can be written generically as:
$$ E^{\rm corrected}_{\rm T} = E^{\rm uncorrected}_{\rm T} - \mathcal{O}(\eta, N_{\mathrm{PV}} , \tau_{\mathrm{bunch}} ), $$
(3)
where \(\mathcal{O}(\eta, N_{\mathrm{PV}} , \tau_{\mathrm{bunch}} )\) corrects for the jet offset due to pile-up.

Due to the varying underlying particle spectrum and the variation in the calorimeter geometry the jet offset is derived as a function of the jet pseudorapidity. The amount of in-time pile-up is parametrised by NPV. The spacing between consecutive bunches, τbunch, is considered, because it can impact the amount by which collisions in previous bunch crossings affect the jet energy measurement.10

The jet offset correction is proportional to the number of constituent towers in a jet as a measure of the jet area. For jets built directly from dynamically-sized topological clusters, for which no clear geometric definition is available, a model is used that describes the average area of a jet in terms of the equivalent number of constituent towers.

8.1.2 Constituent tower multiplicity of jets

The multiplicity of calorimeter towers in jets depends on the internal jet composition and on the presence of pile-up. The average tower multiplicity can be measured in situ.

Figure 5 depicts the distribution of the constituent tower multiplicity for jets based on towers with \(p_{\mathrm{T}} ^{\mathrm{jet}} > 7~ \mathrm{GeV} {}\) as a function of the jet pseudorapidity. The average number of constituent towers is also indicated. This distribution is governed by the change in physical size of calorimeter towers for a constant interval in pseudorapidity, as well as by differences in the noise spectrum for the various calorimeters and sampling regions.
Fig. 5

Distribution of the number constituent calorimeter towers as a function of the jet pseudorapidity for anti-kt jets with R=0.6 and \(p_{\mathrm{T}} ^{\mathrm{jet}} >7~ \mathrm{GeV} \). The black dots indicate the average number of tower constituents

8.1.3 Pile-up offset for towers and jets

The calorimeter tower offset at the EM scale is derived by measuring the average tower transverse energy for all towers in events with NPV=1,2,…,N and comparing directly to events with \(N_{\mathrm {PV}} = N_{\mathrm{PV}} ^{\mathrm{ref}} =1\):
$$ \mathcal{O}_{\rm tower}(\eta, N_{\mathrm{PV}} )=\bigl\langle E^{\rm tower}_{\rm T}(\eta, N_{\mathrm{PV}} )\bigr\rangle - \bigl\langle E^{\rm tower}_{\rm T}\bigl(\eta, N_{\mathrm{PV}} ^{\mathrm{ref}} \bigr)\bigr\rangle, $$
(4)
where the angled brackets denote a statistical average over all events. The average is computed for events at each primary vertex multiplicity. For this measurement non-noise-suppressed calorimeter towers are used (see Sect. 6.1.2) in order to remain sensitive to low energy depositions that may not rise above noise threshold except inside of a jet. The calorimeter tower offset is shown in Fig. 6a for 1≤NPV≤5.
Fig. 6

Tower offset (a) and jet offset (b) at the EM scale as a function of the tower or jet pseudorapidity in bins of the number of reconstructed primary vertices. The jet offset is shown for anti-kt jets with R=0.6. Only statistical uncertainties are shown. They are typically smaller than the marker size

The tower offset can be extrapolated to an EM scale jet offset using:
$$ \mathcal{O}_{\rm jet|tower}(\eta, N_{\mathrm{PV}} )= \mathcal{O}_{\rm tower}(\eta, N_{\mathrm{PV}} ) \cdot A^{\rm jet}, $$
(5)
where \(A^{\rm jet}\) is the jet area that, for jets built from calorimeter towers, can be estimated from the constituent tower multiplicity, \(A^{\rm jet} = N^{\rm jet}_{\rm towers}\). For jets built from topo-clusters, the mean equivalent constituent tower multiplicity (\(A^{\rm jet} = \langle N^{\rm jet}_{\rm towers} \rangle\)) is used.11 The small dependencies of the constituent multiplicity on \(p_{\mathrm{T}} ^{\mathrm{jet}}\) and NPV are neglected in the correction, but incorporated as systematic uncertainties (see Sect. 9.7).

The jet offset for jets with R=0.6 is shown in Fig. 6b.

8.1.4 Track jet based validation and offset correction

Track jets constructed from charged particles originating from the primary hard-scattering vertex matched to the calorimeter jets provide a stable reference that can be used to measure the variation of the calorimeter \(E_{\mathrm{T}} ^{\mathrm{jet}}\) as a function of NPV. It is therefore possible to validate the tower-based offset correction and also to directly estimate the pile-up energy contribution to jets.

As this method is only applicable to jets within the inner detector acceptance, it serves primarily as a cross-check for the tower-based method discussed above. It can also be used, however, to derive a dedicated offset correction that can be applied to jets at energy scales other than the electromagnetic energy scale. Studying the variation of the offset correction as a function of \(p^{\text{track jet}}_{\mathrm{T}}\) can establish the systematic uncertainty of the pile-up correction.

The criterion to match a track jet to a calorimeter jet with R=0.6 is
$$ \Delta R({\rm jet, \text{track jet}}) < 0.4, $$
(6)
where \(\Delta R = \sqrt{(\Delta\eta)^{2} + (\Delta\phi)^{2}}\). The offset is calculated by measuring the average calorimeter jet \(E_{\mathrm{T}} ^{\mathrm{jet}}\) as a function of NPV and the transverse momentum of the matched track jet, \(p^{\text{track jet}}_{\mathrm{T}}\):
$$ \mathcal{O}_{\text{track\ jet}} = \bigl\langle E_{\mathrm{T}} ^{\mathrm{jet}} \bigl( N_{\mathrm{PV}} | p^{\text{track jet}}_{\mathrm{T}} \bigr)\bigr\rangle- \bigl\langle E_{\mathrm{T}} ^{\mathrm{jet}} \bigl( N_{\mathrm{PV}} ^{\mathrm{ref}} | p^{\text{track jet}}_{\mathrm{T}} \bigr)\bigr\rangle. $$
(7)
The reference \(N_{\mathrm{PV}} ^{\mathrm{ref}} = 1 \) is used.

Both tower and topo-cluster jets at the EM-scale are used. The most probable value of the calorimeter jet \(E_{\rm T}\) is determined from a fit using a Landau distribution convolved with a Gaussian for each range of \(p^{\text{track jet}}_{\mathrm{T}}\). A consistent offset of nearly \(\mathcal{O}=0.5~ \mathrm{GeV} \) per vertex is found for |η|<1.9. No systematic trend of the offset as a function of \(p^{\text{track jet}}_{\mathrm{T}}\) is observed.

Figure 7 presents the jet-based offset correction as a function of NPV derived with respect to \(N_{\mathrm{PV}} ^{\mathrm{ref}} =1\) for tower and topo-cluster based jets using the EM and the EM+JES scale. The magnitude of the offset is higher after EM+JES calibration (see Figs. 7c and 7d), and the increase corresponds to the average jet energy correction (see Sect. 8.3).
Fig. 7

Jet offset as a function of the number of primary vertices for several ranges of \(p^{\text{track jet}}_{\mathrm {T}}\) values in bins of track jet pT. The track jet offset is derived for calorimeter tower jets at the EM scale (a), topo-cluster jets at the EM scale (b), calorimeter tower jets at the EM+JES scale (c), and topo-cluster jets at the EM+JES scale (d). Only statistical uncertainties from the fit results are shown. The lines are fits using a linear function

8.2 Jet origin correction

Calorimeter jets are reconstructed using the geometrical centre of the ATLAS detector as reference to calculate the direction of jets and their constituents (see Sect. 6). The jet four-momentum is corrected for each event such that the direction of each topo-cluster points back to the primary hard-scattering vertex. The kinematic observables of each topo-cluster are recalculated using the vector from the primary hard-scattering vertex to the topo-cluster centroid as its direction. The raw jet four-momentum is thereafter redefined as the vector sum of the topo-cluster four-momenta. The origin-corrected pseudorapidity is called \(\eta_{\rm origin}\). This correction improves the angular resolution and results in a small improvement (<1 %) in the jet pT response. The jet energy is unaffected.

8.3 Jet energy correction

The final step of the EM+JES jet calibration restores the reconstructed jet energy to the energy of the Monte Carlo truth jet. Since pile-up effects have already been corrected for, the Monte Carlo samples used to derive the calibration do not include multiple proton-proton interactions.

The calibration is derived using all isolated calorimeter jets that have a matching isolated truth jet within ΔR=0.3. Here, an isolated jet is defined as a jet having no other jet with \(p_{\mathrm{T}} ^{\mathrm{jet}} > 7~ \mathrm{GeV} \) within ΔR=2.5R, where R is the distance parameter of the jet algorithm. A jet is defined to be isolated, if it is isolated with respect to the same jet type, i.e. either a calorimeter or a truth jet.

The final jet energy scale calibration is first parametrised as a function of uncalibrated jet energy and η. Here the detector pseudorapidity is used rather than the origin-corrected η (used by default in physics analyses), since it more directly corresponds to a region of the calorimeter. Energy is used rather than pT, since the calorimeter responds to energy, and as a consequence, the response curves when shown as a function of energy for various η regions can be directly compared. The method to derive this calibration is detailed below.

The EM-scale jet energy response
$$ \mathcal{R} ^{\mathrm{jet}} _ \mathrm{EM} = E^{\mathrm{jet}}_ \mathrm{EM} /E_{\rm truth}^{\rm jet} $$
(8)
for each pair of calorimeter and truth jets is measured in bins of the truth jet energy \(E_{\rm truth}^{\rm jet}\) and the calorimeter jet detector pseudorapidity ηdet.12 For each \((E_{\rm truth}^{\rm jet}, \eta_{\mathrm {det}} )\)-bin, the averaged jet response \(\langle \mathcal {R} ^{\mathrm{jet}} _{ \mathrm{EM} } \rangle\) is defined as the peak position of a Gaussian fit to the \(E^{\mathrm{jet}}_{ \mathrm{EM} } /E_{\rm truth}^{\rm jet}\) distribution. In the same \((E_{\rm truth}^{\rm jet}, \eta_{\mathrm {det}} )\)-bin, in addition, the average jet energy (\(\langle E^{\mathrm{jet}}_{ \mathrm {EM} } \rangle\)) is derived from the mean of the \(E^{\mathrm{jet}}_{ \mathrm{EM} }\) distribution. For a given ηdet-bin k, the jet response calibration function \(\mathcal{F}_{{\rm calib},k}( E^{\mathrm{jet}}_{ \mathrm{EM} } )\) is obtained using a fit of the \(( \langle E^{\mathrm{jet}}_{ \mathrm {EM} } \rangle_{j}, \langle \mathcal{R} ^{\mathrm{jet}} _{ \mathrm {EM} } \rangle_{j})\) values for each \(E_{\rm truth}^{\rm jet}\)-bin j.
The fitting function is parameterised as:
$$ \mathcal{F}_{{\rm calib},k}\bigl( E^{\mathrm{jet}}_ \mathrm{EM} \bigr) = \sum_{i=0}^{N_{\rm max}} a_i \bigl(\ln E^{\mathrm{jet}}_ \mathrm{EM} \bigr)^i, $$
(9)
where ai are free parameters, and Nmax is chosen between 1 and 6 depending on the goodness of the fit.
The final jet energy scale correction that relates the measured calorimeter jet energy to the true energy is then defined as \(1/\mathcal{F}_{{\rm calib}}(E^{\rm calo}_{ \mathrm{EM} })\) in the following:
$$ E^{\rm jet}_{\text{EM+JES}}= \frac{ E^{\mathrm{jet}}_ \mathrm{EM} }{\mathcal{F}_{\rm calib} ( E^{\mathrm{jet}}_ \mathrm{EM} )|_{ \eta_{\mathrm{det}} }}, $$
(10)
where \(\mathcal{F}_{\rm calib}( E^{\mathrm {jet}}_{ \mathrm{EM} } )|_{ \eta_{\mathrm{det}} }\) is the jet response calibration function for the relevant ηdet-bin k.
The average jet energy scale correction \(\langle1/\mathcal {F}_{{\rm calib},k}(E_{\rm calo}^{\rm EM}) \rangle\) is shown as a function of calibrated jet transverse momentum for three jet η-intervals in Fig. 8. In this and the following figures the correction is only shown over the accessible kinematic range, i.e. values for jets above the kinematic limit are not shown.
Fig. 8

Average jet energy scale correction as a function of the calibrated jet transverse momentum for three representative η-intervals obtained from the nominal Monte Carlo simulation sample. This correction corresponds to \(\mathcal{F}_{{\rm calib}}\) in the text. It is only shown over the accessible kinematic range

The calorimeter jet response \(\mathcal{R} ^{\mathrm{jet}} _{ \mathrm{EM} }\) is shown for various energy- and ηdet-bins in Fig. 9. The values of the jet energy correction factors range from about 2.1 at low jet energies in the central region to less than 1.2 for high energy jets in the most forward region.
Fig. 9

Average simulated jet response (\(\mathcal{R} ^{\mathrm{jet}} _{ \mathrm {EM} }\)) at the electromagnetic scale in bins of EM+JES calibrated jet energy and as a function of the detector pseudorapidity ηdet. Also shown are the η-intervals used to evaluate the JES uncertainty (see Table 2). The inverse of the response shown in each bin is equal to the average jet energy scale correction (\(\mathcal{F}_{{\rm calib}}\))

8.4 Jet pseudorapidity correction

After the jet origin and energy corrections the origin-corrected jet η is further corrected for a bias due to poorly instrumented regions of the calorimeter. In these regions topo-clusters are reconstructed with a lower energy with respect to better instrumented regions (see Fig. 9). This causes the jet direction to be biased towards the better instrumented calorimeter regions.

The η-correction is derived as the average difference \(\Delta \eta=\eta_{\rm truth}-\eta_{\rm origin}\) in \((E^{\rm truth}, \eta_{\mathrm{det}} )\)-bins, and is parameterised as a function of the calibrated jet energy \(E^{\rm calo}_{\text{EM+JES}}\) and the uncorrected ηdet. The correction is very small (Δη<0.01) for most regions of the calorimeter but larger in the transition regions. The size of the bias is illustrated as a function of the detector pseudorapidity |ηdet| and EM+JES calibrated jet energy in Fig. 10.
Fig. 10

Difference between the jet pseudorapidity calculated using an origin correction and the true jet pseudorapidity in bins of the calorimeter jet energy calibrated with the EM+JES scheme as a function of the detector pseudorapidity |ηdet|

9 Jet energy scale uncertainties for the EM+JES scheme

The JES systematic uncertainty is derived combining information from the single hadron response measured in situ and single pion test-beam measurements, uncertainties on the amount of material of the ATLAS detector, the description of the electronic noise, and the Monte Carlo modelling used in the event generation. Dedicated Monte Carlo simulation test samples are generated with different conditions with respect to the nominal Monte Carlo sample described in Sect. 4.3. These variations are expected to provide an estimate of the systematic effects contributing to the JES uncertainty.

The pseudorapidity bins used for the estimate of the JES uncertainty divide the ATLAS detector in the eight η-regions specified in Table 2 and Fig. 9.
Table 2

Detector regions used for the JES uncertainty estimate

η region

ATLAS detector regions

|η|<0.3

Central Barrel

0.3≤|η|<0.8

0.8≤|η|<1.2

Barrel-Endcap Transition

1.2≤|η|<2.1

2.1≤|η|<2.8

Endcap

2.8≤|η|<3.2

Endcap-Forward Transition

3.2≤|η|<3.6

3.6≤|η|<4.5

Forward

The JES systematic uncertainty for all jets with pseudorapidity |η|>0.8 is determined using the JES uncertainty for the central barrel region (0.3≤|η|<0.8) as a baseline, with a contribution from the relative calibration of the jets with respect to the central barrel region. This choice is motivated by the good knowledge of the detector geometry in the central region, and by the use of pion response measurements in the ATLAS combined test-beam, which used a full slice of the ATLAS barrel detector, for the estimate of the calorimeter response uncertainties. The region 0.3≤|η|<0.8 is the largest fully instrumented |η| region considered where combined test-beam results, used to estimate the calorimeter uncertainty, are available for the entire pseudorapidity range.

This section describes the sources of systematic uncertainties and their effect on the response of EM+JES calibrated jets. In Sect. 9.1, the selection of jets used to derive Monte Carlo based components of the JES systematic uncertainty is discussed. The contributions to the JES systematics due to the following effects are then described:
  1. 1.

    JES calibration method (Sect. 9.2).

     
  2. 2.

    Calorimeter response (Sect. 9.3).

     
  3. 3.

    Detector simulation (Sect. 9.4).

     
  4. 4.

    Physics model and parameters employed in the Monte Carlo event generator (Sect. 9.5).

     
  5. 5.

    Relative calibration for jets with |η|>0.8 (Sect. 9.6).

     
  6. 6.

    Additional proton-proton collisions (pile-up) (Sect. 9.7).

     

Section 9.8 discusses how the final uncertainties are calculated. Additional uncertainties such as those for close-by jets are mentioned in Sect. 9.9 and discussed in more detail in Sect. 17.

9.1 Jet response definition

The average energy or pT response, defined as
$$ \bigl\langle \mathcal{R} ^{\mathrm{jet}} \bigr\rangle =\bigl\langle E^{\mathrm{jet}} / E^{\mathrm{truth}} \bigr\rangle \quad \mbox{or}\quad \bigl\langle\mathcal{R}\bigl( p_{\mathrm{T}} ^{\mathrm{jet}} \bigr) \bigr\rangle = \bigl\langle p_{\mathrm{T}} ^{\mathrm{jet}} / p_{\mathrm{T}} ^{\mathrm{truth}} \bigr\rangle, $$
(11)
is obtained as the peak position from a Gaussian fit to the distribution of the ratio of reconstructed energy, respectively pT, for reconstructed and truth jets by matching isolated calorimeter jets to Monte Carlo truth jets as described in Sect. 8.3, but without the isolation cut for truth jets.13 This is done separately for the nominal and each of the alternative Monte Carlo samples. Only MC truth jets with \(p_{\mathrm{T}} ^{\mathrm{truth}} {}>15~ \mathrm{GeV} \), and calorimeter jets with \(p_{\mathrm{T}} ^{\mathrm {jet}} >