Abstract
Information deformation and loss in jet clustering are one of the major limitations for precisely measuring hadronic events at future e−e+ colliders. Because of their dominance in data, the measurements of such events are crucial for advancing the precision frontier of Higgs and electroweak physics in the next decades. We show that this difficulty can be well-addressed by synergizing the event-level information into the data analysis, with the techniques of deep neutral network. In relation to this, we introduce a CMB-like observable scheme, where the event-level kinematics is encoded as Fox-Wolfram (FW) moments at leading order and multi-spectra of spherical harmonics at higher orders. Then we develop a series of jet-level (w/ and w/o the FW moments) and event-level classifiers, and analyze their sensitivity performance comparatively with two-jet and four-jet events. As an application, we analyze measuring Higgs decay width at e−e+ colliders with the data of 5ab−1@240GeV. The precision obtained is significantly better than the baseline ones presented in documents. We expect this strategy to be applied to many other hadronic- event measurements at future e−e+ colliders, and to open a new angle for evaluating their physics capability.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
G. Taylor, A Perspective of the Future for HEP, (2020) [http://ias.ust.hk/program/shared doc/2020/202001hep/conf/20200120 lt Geoffrey TAYLOR.pdf ].
J. Fan, M. Reece and L.-T. Wang, Possible Futures of Electroweak Precision: ILC, FCC-ee, and CEPC, JHEP 09 (2015) 196 [arXiv:1411.1054] [INSPIRE].
A. Banfi, H. McAslan, P.F. Monni and G. Zanderighi, A general method for the resummation of event-shape distributions in e+ e− annihilation, JHEP 05 (2015) 102 [arXiv:1412.2126] [INSPIRE].
D. d’Enterria, Physics at the FCC-ee, in 17th Lomonosov Conference on Elementary Particle Physics, pp. 182–191 (2017) [DOI] [arXiv:1602.05043] [INSPIRE].
M.A. Fedderke, T. Lin and L.-T. Wang, Probing the fermionic Higgs portal at lepton colliders, JHEP 04 (2016) 160 [arXiv:1506.05465] [INSPIRE].
H. Khanpour and M. Mohammadi Najafabadi, Constraining Higgs boson effective couplings at electron-positron colliders, Phys. Rev. D 95 (2017) 055026 [arXiv:1702.00951] [INSPIRE].
C. Cai, Z.-H. Yu and H.-H. Zhang, CEPC Precision of Electroweak Oblique Parameters and Weakly Interacting Dark Matter: the Fermionic Case, Nucl. Phys. B 921 (2017) 181 [arXiv:1611.02186] [INSPIRE].
W.H. Chiu, S.C. Leung, T. Liu, K.-F. Lyu and L.-T. Wang, Probing 6D operators at future e− e+ colliders, JHEP 05 (2018) 081 [arXiv:1711.04046] [INSPIRE].
N. Chen, T. Han, S. Su, W. Su and Y. Wu, Type-II 2HDM under the Precision Measurements at the Z -pole and a Higgs Factory, JHEP 03 (2019) 023 [arXiv:1808.02037] [INSPIRE].
G. Durieux, C. Grojean, J. Gu and K. Wang, The leptonic future of the Higgs, JHEP 09 (2017) 014 [arXiv:1704.02333] [INSPIRE].
T. Barklow et al., Improved Formalism for Precision Higgs Coupling Fits, Phys. Rev. D 97 (2018) 053003 [arXiv:1708.08912] [INSPIRE].
S. Di Vita et al., A global view on the Higgs self-coupling at lepton colliders, JHEP 02 (2018) 178 [arXiv:1711.03978] [INSPIRE].
J. Gu, H. Li, Z. Liu, S. Su and W. Su, Learning from Higgs Physics at Future Higgs Factories, JHEP 12 (2017) 153 [arXiv:1709.06103] [INSPIRE].
S.-F. Ge, H.-J. He and R.-Q. Xiao, Probing new physics scales from Higgs and electroweak observables at e+ e− Higgs factory, JHEP 10 (2016) 007 [arXiv:1603.03385] [INSPIRE].
S.-F. Ge, H.-J. He and R.-Q. Xiao, Testing Higgs coupling precision and new physics scales at lepton colliders, arXiv:1612.02718 [INSPIRE].
J. Ellis, S.-F. Ge, H.-J. He and R.-Q. Xiao, Probing the scale of new physics in the Z Z γ coupling at e+ e− colliders, Chin. Phys. C 44 (2020) 063106 [arXiv:1902.06631] [INSPIRE].
F. An et al., Precision Higgs physics at the CEPC, Chin. Phys. C 43 (2019) 043002 [arXiv:1810.09037] [INSPIRE].
FCC collaboration, FCC Physics Opportunities : Future Circular Collider Conceptual Design Report Volume 1, Eur. Phys. J. C 79 (2019) 474 [INSPIRE].
CEPC Study Group collaboration, CEPC Conceptual Design Report: Volume 2 — Physics & Detector, arXiv:1811.10545 [INSPIRE].
H. Abramowicz et al., Higgs physics at the CLIC electron-positron linear collider, Eur. Phys. J. C 77 (2017) 475 [arXiv:1608.07538] [INSPIRE].
H. Ono and A. Miyamoto, A study of measurement precision of the Higgs boson branching ratios at the International Linear Collider, Eur. Phys. J. C 73 (2013) 2343 [arXiv:1207.0300] [INSPIRE].
J. Tian and K. Fujii, Measurement of Higgs boson couplings at the International Linear Collider, Nucl. Part. Phys. Proc. 273-275 (2016) 826 [INSPIRE].
ILD Design Study Group collaboration, HZ Recoil Mass and Cross Section Analysis in ILD, arXiv:1202.1439 [INSPIRE].
Y. Zhu and M. Ruan, Performance study of the separation of the full hadronic WW and ZZ events at the CEPC, arXiv:1812.09478 [INSPIRE].
G. Li, Z. Li, Y. Wang and Y. Wang, Improving the measurement of the Higgs boson-gluon coupling using convolutional neural networks at e+ e− colliders, Phys. Rev. D 100 (2019) 116013 [arXiv:1901.09391] [INSPIRE].
M. Andrews et al., End-to-end jet classification of quarks and gluons with the CMS Open Data, Nucl. Instrum. Meth. A 977 (2020) 164304 [arXiv:1902.08276] [INSPIRE].
G. Kasieczka, N. Kiefer, T. Plehn and J.M. Thompson, Quark-Gluon Tagging: Machine Learning vs Detector, SciPost Phys. 6 (2019) 069 [arXiv:1812.09223] [INSPIRE].
P.T. Komiske, E.M. Metodiev and M.D. Schwartz, Deep learning in color: towards automated quark/gluon jet discrimination, JHEP 01 (2017) 110 [arXiv:1612.01551] [INSPIRE].
T. Cheng, Recursive Neural Networks in Quark/Gluon Tagging, Comput. Softw. Big Sci. 2 (2018) 3 [arXiv:1711.02633] [INSPIRE].
P.T. Komiske, E.M. Metodiev and J. Thaler, Energy Flow Networks: Deep Sets for Particle Jets, JHEP 01 (2019) 121 [arXiv:1810.05165] [INSPIRE].
J. Gallicchio and M.D. Schwartz, Seeing in Color: Jet Superstructure, Phys. Rev. Lett. 105 (2010) 022001 [arXiv:1001.5027] [INSPIRE].
M. Cacciari, G.P. Salam and G. Soyez, The anti-kt jet clustering algorithm, JHEP 04 (2008) 063 [arXiv:0802.1189] [INSPIRE].
S. Catani, Y.L. Dokshitzer, M. Olsson, G. Turnock and B.R. Webber, New clustering algorithm for multi-jet cross-sections in e+ e− annihilation, Phys. Lett. B 269 (1991) 432 [INSPIRE].
S.D. Ellis, Z. Kunszt and D.E. Soper, Jets at hadron colliders at order \( {\alpha}_s^3 \): A Look inside, Phys. Rev. Lett. 69 (1992) 3615 [hep-ph/9208249] [INSPIRE].
E. Farhi, A QCD Test for Jets, Phys. Rev. Lett. 39 (1977) 1587 [INSPIRE].
I.W. Stewart, F.J. Tackmann and W.J. Waalewijn, N-Jettiness: An Inclusive Event Shape to Veto Jets, Phys. Rev. Lett. 105 (2010) 092002 [arXiv:1004.2489] [INSPIRE].
G. Parisi, Super Inclusive Cross-Sections, Phys. Lett. B 74 (1978) 65 [INSPIRE].
C.F. Berger, T. Kucs and G.F. Sterman, Event shape/energy flow correlations, Phys. Rev. D 68 (2003) 014012 [hep-ph/0303051] [INSPIRE].
V. Mateu, I.W. Stewart and J. Thaler, Power Corrections to Event Shapes with Mass-Dependent Operators, Phys. Rev. D 87 (2013) 014025 [arXiv:1209.3781] [INSPIRE].
S. Catani, G. Turnock and B.R. Webber, Jet broadening measures in e+ e− annihilation, Phys. Lett. B 295 (1992) 269 [INSPIRE].
M. Dasgupta and G.P. Salam, Event shapes in e+ e− annihilation and deep inelastic scattering, J. Phys. G 30 (2004) R143 [hep-ph/0312283] [INSPIRE].
A. Banfi, G.P. Salam and G. Zanderighi, Phenomenology of event shapes at hadron colliders, JHEP 06 (2010) 038 [arXiv:1001.4082] [INSPIRE].
C. Cesarotti and J. Thaler, A Robust Measure of Event Isotropy at Colliders, JHEP 08 (2020) 084 [arXiv:2004.06125] [INSPIRE].
G.C. Fox and S. Wolfram, Observables for the Analysis of Event Shapes in e+ e− Annihilation and Other Processes, Phys. Rev. Lett. 41 (1978) 1581 [INSPIRE].
D. Krohn, M.D. Schwartz, T. Lin and W.J. Waalewijn, Jet Charge at the LHC, Phys. Rev. Lett. 110 (2013) 212001 [arXiv:1209.2421] [INSPIRE].
I. Moult, L. Necib and J. Thaler, New Angles on Energy Correlation Functions, JHEP 12 (2016) 153 [arXiv:1609.07483] [INSPIRE].
M. Liguori, E. Sefusatti, J.R. Fergusson and E.P.S. Shellard, Primordial non-Gaussianity and Bispectrum Measurements in the Cosmic Microwave Background and Large-Scale Structure, Adv. Astron. 2010 (2010) 980523 [arXiv:1001.4707] [INSPIRE].
M. Andrews, M. Paulini, S. Gleyzer and B. Poczos, End-to-End Event Classification of High-Energy Physics Data, J. Phys. Conf. Ser. 1085 (2018) 042022 [INSPIRE].
M. Andrews, M. Paulini, S. Gleyzer and B. Poczos, End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC, Comput. Softw. Big Sci. 4 (2020) 6 [arXiv:1807.11916] [INSPIRE].
P.T. Komiske, R. Mastandrea, E.M. Metodiev, P. Naik and J. Thaler, Exploring the Space of Jets with CMS Open Data, Phys. Rev. D 101 (2020) 034009 [arXiv:1908.08542] [INSPIRE].
J.W. Monk, Deep Learning as a Parton Shower, JHEP 12 (2018) 021 [arXiv:1807.03685] [INSPIRE].
L. de Oliveira, M. Kagan, L. Mackey, B. Nachman and A. Schwartzman, Jet-images — deep learning edition, JHEP 07 (2016) 069 [arXiv:1511.05190] [INSPIRE].
G. Kasieczka, T. Plehn, M. Russell and T. Schell, Deep-learning Top Taggers or The End of QCD?, JHEP 05 (2017) 006 [arXiv:1701.08784] [INSPIRE].
J. Lin, M. Freytsis, I. Moult and B. Nachman, Boosting \( H\to b\overline{b} \) with Machine Learning, JHEP 10 (2018) 101 [arXiv:1807.10768] [INSPIRE].
J.H. Kim, K. Kong, K.T. Matchev and M. Park, Probing the Triple Higgs Self-Interaction at the Large Hadron Collider, Phys. Rev. Lett. 122 (2019) 091801 [arXiv:1807.11498] [INSPIRE].
J.H. Kim, M. Kim, K. Kong, K.T. Matchev and M. Park, Portraying Double Higgs at the Large Hadron Collider, JHEP 09 (2019) 047 [arXiv:1904.08549] [INSPIRE].
P.T. Komiske, E.M. Metodiev, B. Nachman and M.D. Schwartz, Pileup Mitigation with Machine Learning (PUMML), JHEP 12 (2017) 051 [arXiv:1707.08600] [INSPIRE].
A. Andreassen, I. Feige, C. Frye and M.D. Schwartz, JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics, Eur. Phys. J. C 79 (2019) 102 [arXiv:1804.09720] [INSPIRE].
J. Ren, L. Wu and J.M. Yang, Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC, Phys. Lett. B 802 (2020) 135198 [arXiv:1901.05627] [INSPIRE].
J. Arjona Martínez, O. Cerri, M. Pierini, M. Spiropulu and J.-R. Vlimant, Pileup mitigation at the Large Hadron Collider with graph neural networks, Eur. Phys. J. Plus 134 (2019) 333 [arXiv:1810.07988] [INSPIRE].
S. Farrell et al., Novel deep learning methods for track reconstruction, in 4th International Workshop Connecting The Dots 2018, (2018) [arXiv:1810.06111] [INSPIRE].
M. Abdughani, J. Ren, L. Wu and J.M. Yang, Probing stop pair production at the LHC with graph neural networks, JHEP 08 (2019) 055 [arXiv:1807.09088] [INSPIRE].
H. Qu and L. Gouskos, ParticleNet: Jet Tagging via Particle Clouds, Phys. Rev. D 101 (2020) 056019 [arXiv:1902.08570] [INSPIRE].
J. Zhou, G. Cui, Z. Zhang, C. Yang, Z. Liu and M. Sun, Graph neural networks: A review of methods and applications, arXiv:1812.08434.
A. Paszke et al., Pytorch: An imperative style, high-performance deep learning library, in Advances in Neural Information Processing Systems 32, H. Wallach et al. eds., pp. 8024–8035, Curran Associates, Inc. (2019).
K. He, X. Zhang, S. Ren and J. Sun, Deep Residual Learning for Image Recognition, arXiv:1512.03385 [INSPIRE].
C. Chen et al., Fast simulation of the CEPC detector with Delphes, arXiv:1712.09517 [INSPIRE].
M.A.E. Fontanesi and L. Pezzotti, FCC-ee IDEA detector model for Delphes, (2019).
D.P. Kingma and J. Ba, Adam: A Method for Stochastic Optimization, in 3rd International Conference on Learning Representations, ICLR, (2015) [arXiv:1412.6980] [INSPIRE].
J. Alwall, M. Herquet, F. Maltoni, O. Mattelaer and T. Stelzer, MadGraph 5: Going Beyond, JHEP 06 (2011) 128 [arXiv:1106.0522] [INSPIRE].
T. Sjöstrand, S. Mrenna and P.Z. Skands, A Brief Introduction to PYTHIA 8.1, Comput. Phys. Commun. 178 (2008) 852 [arXiv:0710.3820] [INSPIRE].
M. Cacciari, G.P. Salam and G. Soyez, FastJet User Manual, Eur. Phys. J. C 72 (2012) 1896 [arXiv:1111.6097] [INSPIRE].
M. Ruan et al., Reconstruction of physics objects at the Circular Electron Positron Collider with Arbor, Eur. Phys. J. C 78 (2018) 426 [arXiv:1806.04879] [INSPIRE].
DELPHES 3 collaboration, DELPHES 3, A modular framework for fast simulation of a generic collider experiment, JHEP 02 (2014) 057 [arXiv:1307.6346] [INSPIRE].
FCC collaboration, FCC-ee: The Lepton Collider : Future Circular Collider Conceptual Design Report Volume 2, Eur. Phys. J. ST 228 (2019) 261 [INSPIRE].
D. Guest, J. Collado, P. Baldi, S.-C. Hsu, G. Urban and D. Whiteson, Jet Flavor Classification in High-Energy Physics with Deep Neural Networks, Phys. Rev. D 94 (2016) 112002 [arXiv:1607.08633] [INSPIRE].
A. Hocker et al., TMVA — Toolkit for Multivariate Data Analysis, physics/0703039 [INSPIRE].
C. Dürig, K. Fujii, J. List and J. Tian, Model Independent Determination of H W W coupling and Higgs total width at ILC, in International Workshop on Future Linear Colliders, (2014) [arXiv:1403.7734] [INSPIRE].
K. Fujii et al., Physics Case for the 250 GeV Stage of the International Linear Collider, arXiv:1710.07621 [INSPIRE].
C. Bernaciak, M.S.A. Buschmann, A. Butter and T. Plehn, Fox-Wolfram Moments in Higgs Physics, Phys. Rev. D 87 (2013) 073014 [arXiv:1212.4436] [INSPIRE].
C. Bernaciak, B. Mellado, T. Plehn, P. Schichtel and X. Ruan, Improving Higgs plus Jets analyses through Fox-Wolfram Moments, Phys. Rev. D 89 (2014) 053006 [arXiv:1311.5891] [INSPIRE].
G. Cowan, K. Cranmer, E. Gross and O. Vitells, Asymptotic formulae for likelihood-based tests of new physics, Eur. Phys. J. C 71 (2011) 1554 [Erratum ibid. 73 (2013) 2501] [arXiv:1007.1727] [INSPIRE].
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
ArXiv ePrint: 2004.15013
Rights and permissions
Open Access . This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.
About this article
Cite this article
Li, L., Li, YY., Liu, T. et al. Learning physics at future e−e+ colliders with machine. J. High Energ. Phys. 2020, 18 (2020). https://doi.org/10.1007/JHEP10(2020)018
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/JHEP10(2020)018