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Benchmark data and model independent event classification for the large hadron collider

Aarrestad, Thea (author)
CERN
van Beekveld, Melissa (author)
University of Oxford
Bona, Marcella (author)
Queen Mary University
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Boveia, Antonio (author)
Ohio State University
Caron, Sascha (author)
Dutch National Institute for Subatomic Physics (NIKHEF)
Davies, Joe (author)
Queen Mary University
De Simone, Andrea (author)
INFN Section of Trieste,International School For Advanced Studies (sissa/isas)
Doglioni, Caterina (author)
Lund University,Lunds universitet,Partikel- och kärnfysik,Fysiska institutionen,Institutioner vid LTH,Lunds Tekniska Högskola,Particle and nuclear physics,Department of Physics,Departments at LTH,Faculty of Engineering, LTH
Duarte, Javier M. (author)
University of California, San Diego
Farbin, Amir (author)
University of Texas at Arlington
Gupta, Honey (author)
Google Inc.
Hendriks, Luc (author)
Dutch National Institute for Subatomic Physics (NIKHEF)
Heinrich, Lukas (author)
CERN
Howarth, James (author)
University of Glasgow
Jawahar, Pratik (author)
CERN,Worcester Polytechnic Institute
Jueid, Adil (author)
Konkuk University
Lastow, Jessica (author)
Lund University,Lunds universitet,MAX IV-laboratoriet,MAX IV Laboratory
Leinweber, Adam (author)
University of Adelaide
Mamuzic, Judita (author)
CSIC-UV - Instituto de Física Corpuscular (IFIC)
Merényi, Erzsébet (author)
Rice University
Morandini, Alessandro (author)
RWTH Aachen University
Moskvitina, Polina (author)
Dutch National Institute for Subatomic Physics (NIKHEF)
Nellist, Clara (author)
Dutch National Institute for Subatomic Physics (NIKHEF)
Ngadiuba, Jennifer (author)
Fermi National Accelerator Laboratory,California Institute of Technology
Ostdiek, Bryan (author)
Institute for Artificial Intelligence and Fundamental Interactions (NSF AI),Harvard University
Pierini, Maurizio (author)
CERN
Ravina, Baptiste (author)
University of Glasgow
de Austri, Roberto R. (author)
CSIC-UV - Instituto de Física Corpuscular (IFIC)
Sekmen, Sezen (author)
Kyungpook National University
Touranakou, Mary (author)
CERN,National and Kapodistrian University of Athens
Vaškevičiūte, Marija (author)
University of Glasgow
Vilalta, Ricardo (author)
University of Houston
Vlimant, Jean Roch (author)
California Institute of Technology
Verheyen, Rob (author)
University College London
White, Martin (author)
University of Adelaide
Wulff, Eric (author)
Lund University
Wallin, Erik (author)
Lund University
Wozniak, Kinga A. (author)
University of Vienna,CERN
Zhang, Zhongyi (author)
Dutch National Institute for Subatomic Physics (NIKHEF)
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 (creator_code:org_t)
2022
2022
English.
In: SciPost Physics. - 2542-4653. ; 12:1
Related links:
http://dx.doi.org/10... (free)
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https://lup.lub.lu.s...
https://doi.org/10.2...
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  • Journal article (peer-reviewed)
Abstract Subject headings
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  • 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 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.

Subject headings

NATURVETENSKAP  -- Fysik -- Acceleratorfysik och instrumentering (hsv//swe)
NATURAL SCIENCES  -- Physical Sciences -- Accelerator Physics and Instrumentation (hsv//eng)

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