CaltechAUTHORS
Published December 2021 | Version Accepted Version + Submitted
Journal Article Open

The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics

Creators

  • 1. ROR icon Universität Hamburg
  • 2. ROR icon Lawrence Berkeley National Laboratory
  • 3. ROR icon University of California, Berkeley
  • 4. ROR icon Rutgers, The State University of New Jersey
  • 5. ROR icon Johns Hopkins University
  • 6. ROR icon Google (United States)
  • 7. ROR icon Reed College
  • 8. ROR icon Jožef Stefan Institute
  • 9. ROR icon Columbia University
  • 10. ROR icon University of Zurich
  • 11. ROR icon SLAC National Accelerator Laboratory
  • 12. ROR icon University of Aveiro
  • 13. ROR icon Heidelberg University
  • 14. ROR icon University of Clermont Auvergne
  • 15. ROR icon University of Kansas
  • 16. ROR icon University of California, San Diego
  • 17. ROR icon Massachusetts Institute of Technology
  • 18. ROR icon University of Ljubljana
  • 19. ROR icon University of Sussex
  • 20. ROR icon University of Genoa
  • 21. ROR icon University of Michigan–Ann Arbor
  • 22. ROR icon Institute for Theoretical Physics
  • 23. ROR icon European Organization for Nuclear Research
  • 24. ROR icon Institute for Corpuscular Physics
  • 25. ROR icon National University of General San Martín
  • 26. ROR icon California Institute of Technology

Abstract

A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.

Additional Information

© 2021 IOP Publishing Ltd. Received 21 February 2021; Revised 27 October 2021; Accepted 4 November 2021; Published 7 December 2021. We thank the organizers and participants in the ML4Jets2020 workshop hosted at New York University and at the anomaly detection workshop hosted (virtually) by the University of Hamburg for many interesting discussions at the Winter and Summer Olympics, respectively. B Nachman and G Kasieczka are grateful to the NHETC Visitor Program at Rutgers University for the generous support and hospitality during the spring of 2019 where the idea for the LHC Olympics 2020 was conceived. We would also like to thank Kastubh Agashe for discussions about the KK gluon model that became the signal in BB3. A Kahn, J Gonski, D Williams, and G Brooijmans are supported by the National Science Foundation (NSF) under Grant No. PHY-2013070. I Ochoa is supported by the fellowship LCF/BQ/PI20/11760025 from 'la Caixa' Foundation (ID 100010434) and by the European Union's Horizon 2020 research and innovation programme under the Marie Skłlodowska-Curie Grant Agreement No. 847648. S E Park, S Udrescu, M Yunus, P Harris are supported by the NSF Grants #1934700 and #1931469. Cloud credits for training were supported by the Internet2/NSF Grant #190444. V Mikuni and F Canelli are supported in part by the Swiss National Science Foundation (SNF) under Contract No. 200020-182037. F F Freitas is supported by the Center for Research and Development in Mathematics and Applications (CIDMA) through the Portuguese Foundation for Science and Technology (FCT-Fundação para a Ciência e a Tecnologia), references UIDB/04106/2020 and UIDP/04106/2020 and the project PTDC/FIS-PAR/31000/2017. C K Khosa is supported by the Royal Society, UK under the Newton International Fellowship programme (NF171488). K Benkendorfer was supported in part by NSF PHY REU Grant 1949923. B Bortolato, B Dillon, A Matevc, J Kamenik, A Smolkovic acknowledge the financial support from the Slovenian Research Agency (research core funding No. P1-0035 and J1-8137). D A Faroughy is supported by SNF under contract 200021-159720. M Szewc would like to thank the Jozef Stefan Institute for its enormous hospitality. P Komiske, E Metodiev, N Sarda, and J Thaler are supported by the Office of Nuclear Physics of the U.S. Department of Energy (DOE) under Grant DE-SC-0011090 and by the DOE Office of High Energy Physics under Grant DE-SC0012567. N Sarda was additionally supported by the QCRI-CSAIL Computer Research Program. P Komiske, E Metodiev, N Sarda, and J Thaler are grateful to Benjamin Nachman and Justin Solomon for helpful conversations. B Nachman and J Collins were supported by the DOE under contracts DE-AC02-05CH11231 and DE-AC02-76SF00515, respectively. P Martín-Ramiro acknowledges Berkeley LBNL, where part of this work has been developed. P Martín-Ramiro further acknowledges support from the Spanish Research Agency (Agencia Estatal de Investigación) through the contract FPA2016-78022-P and IFT Centro de Excelencia Severo Ochoa under Grant SEV-2016-0597. P Martín-Ramiro also received funding/support from the European Union's Horizon 2020 research and innovation programme under the Marie Skłlodowska-Curie Grant Agreement No. 690575 (RISE InvisiblesPlus). S Tsan, J Duarte, J-R Vilmant, and M Pierini thank the University of California San Diego Triton Research and Experiential Learning Scholars (TRELS) program for supporting this research, CENIC for the 100 Gpbs networks, and Joosep Pata for helpful discussions. They are additionally supported in part by NSF awards CNS-1730158, ACI-1540112, ACI-1541349, OAC-1826967, the University of California Office of the President, and the University of California San Diego's California Institute for Telecommunications and Information Technology/Qualcomm Institute. J Duarte is supported by the DOE, Office of Science, Office of High Energy Physics Early Career Research program under Award No. DE-SC0021187. M Pierini is supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant Agreement No. 772369). J-R Vilmant is partially supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant Agreement No. 772369) and by the DOE, Office of Science, Office of High Energy Physics under Award No. DE-SC0011925, DE-SC0019227, and DE-AC02-07CH11359. D Shih is supported by DOE Grant DOE-SC0010008. GK acknowledges support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy—EXC 2121 'Quantum Universe'—390833306. Data availability statement: The data that support the findings of this study are openly available at the following URL/DOI: https://zenodo.org/record/4536624#.YDLWKy2ZMXw.

Attached Files

Accepted Version - Kasieczka+et+al_2021_Rep._Prog._Phys._10.1088_1361-6633_ac36b9.pdf

Submitted - 2101.08320.pdf

Files

2101.08320.pdf

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Additional details

Identifiers

Eprint ID
112239
DOI
10.1088/1361-6633/ac36b9
Resolver ID
CaltechAUTHORS:20211206-230203559

Related works

Describes
10.1088/1361-6633/ac36b9 (DOI)
https://arxiv.org/abs/2101.08320 (URL)
https://zenodo.org/record/4536624#.YDLWKy2ZMXw (URL)

Funding

NSF
PHY-2013070
La Caixa Foundation
LCF/BQ/PI20/11760025
La Caixa Foundation
100010434
Marie Curie Fellowship
847648
NSF
OAC-1934700
NSF
OAC-1931469
NSF
OAC-190444
Swiss National Science Foundation (SNSF)
200020-182037
Fundação para a Ciência e a Tecnologia (FCT)
UIDB/04106/2020
Fundação para a Ciência e a Tecnologia (FCT)
UIDP/04106/2020
Fundação para a Ciência e a Tecnologia (FCT)
PTDC/FIS-PAR/31000/2017
Royal Society
NF171488
NSF
PHY-1949923
Slovenian Research Agency
P1-0035
Slovenian Research Agency
J1-8137
Swiss National Science Foundation (SNSF)
200021-159720
Department of Energy (DOE)
DE-SC-0011090
Department of Energy (DOE)
DE-SC0012567
Qatar Computing Research Institute
Department of Energy (DOE)
DE-AC02-05CH11231
Department of Energy (DOE)
DE-AC02-76SF00515
Lawrence Berkeley National Laboratory
Agencia Estatal de Investigación
FPA2016-78022-P
Centro de Excelencia Severo Ochoa
SEV-2016-0597
Marie Curie Fellowship
690575
NSF
CNS-1730158
NSF
ACI-1540112
NSF
ACI-1541349
NSF
OAC-1826967
University of California
University of California, San Diego
Department of Energy (DOE)
DE-SC0021187
European Research Council (ERC)
772369
Department of Energy (DOE)
DE-SC0011925
Department of Energy (DOE)
DE-SC0019227
Department of Energy (DOE)
DE-AC02-07CH11359
Department of Energy (DOE)
DOE-SC0010008
Deutsche Forschungsgemeinschaft (DFG)
390833306

Dates

Created
2021-12-07
Created from EPrint's datestamp field
Updated
2022-07-12
Created from EPrint's last_modified field