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Title: The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider

Journal Article · · SciPost Physics
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  1. European Organization for Nuclear Research
  2. Rudolf Peierls Centre for Theoretical Physics, University of Oxford
  3. Queen Mary University of London
  4. The Ohio State University
  5. National Institute for Subatomic Physics
  6. International School for Advanced Studies, National Institute for Nuclear Physics
  7. Lund University
  8. University of California, San Diego
  9. The University of Texas at Arlington
  10. Google
  11. University of Glasgow
  12. European Organization for Nuclear Research, Worcester Polytechnic Institute
  13. Konkuk University
  14. University of Adelaide
  15. Institute for Corpuscular Physics
  16. Rice University
  17. RWTH Aachen University
  18. California Institute of Technology, Fermi National Accelerator Laboratory
  19. Harvard University, The NSF AI Institute for Artificial Intelligence and Fundamental Interactions
  20. Kyungpook National University
  21. European Organization for Nuclear Research, National and Kapodistrian University of Athens
  22. University of Houston
  23. California Institute of Technology
  24. University College London
  25. University of Vienna, European Organization for Nuclear Research
+ Show Author Affiliations

We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of >1 billion simulated LHC events corresponding to 10\, fb^{-1} 10 f b − 1 of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.

Research Organization:
Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); The Ohio State University, Columbus, OH (United States)
Sponsoring Organization:
Australian Research Council (ARC); European Research Council (ERC); National Research Foundation of Korea (NRF); National Science Foundation (NSF); Science and Technology Facilities Council (STFC); USDOE; USDOE Office of Science (SC), High Energy Physics (HEP)
Grant/Contract Number:
AC02-07CH11359; SC0011726; SC0011925; SC0013607; SC0019227; SC0021187; SC0021396
OSTI ID:
1842562
Report Number(s):
FERMILAB-PUB--21-285-CMS; arXiv:2105.14027; 043
Journal Information:
SciPost Physics, Journal Name: SciPost Physics Journal Issue: 1 Vol. 12; ISSN 2542-4653
Publisher:
Stichting SciPostCopyright Statement
Country of Publication:
Netherlands
Language:
English

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