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

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dc.citation.articleNumber043
dc.citation.journalTitleSciPost Physics
dc.citation.volumeNumber12
dc.contributor.authorAarrestad, Thea
dc.contributor.authorvan Beekveld, Melissa
dc.contributor.authorBona, Marcella
dc.contributor.authorBoveia, Antonio
dc.contributor.authorCaron, Sascha
dc.contributor.authorDavies, Joe
dc.contributor.authorde Simone, Andrea
dc.contributor.authorDoglioni, Caterina
dc.contributor.authorDuarte, Javier
dc.contributor.authorFarbin, Amir
dc.contributor.authorGupta, Honey
dc.contributor.authorHendriks, Luc
dc.contributor.authorHeinrich, Lukas A.
dc.contributor.authorHowarth, James
dc.contributor.authorJawahar, Pratik
dc.contributor.authorJueid, Adil
dc.contributor.authorLastow, Jessica
dc.contributor.authorLeinweber, Adam
dc.contributor.authorMamuzic, Judita
dc.contributor.authorMerényi, Erzsébet
dc.contributor.authorMorandini, Alessandro
dc.contributor.authorMoskvitina, Polina
dc.contributor.authorNellist, Clara
dc.contributor.authorNgadiuba, Jennifer
dc.contributor.authorOstdiek, Bryan
dc.contributor.authorPierini, Maurizio
dc.contributor.authorRavina, Baptiste
dc.contributor.authorRuiz de Austri, Roberto
dc.contributor.authorSekmen, Sezen
dc.contributor.authorTouranakou, Mary
dc.contributor.authorVaškeviciute, Marija
dc.contributor.authorVilalta, Ricardo
dc.contributor.authorVlimant, Jean-Roch
dc.contributor.authorVerheyen, Rob
dc.contributor.authorWhite, Martin
dc.contributor.authorWulff, Eric
dc.contributor.authorWallin, Erik
dc.contributor.authorWozniak, Kinga A.
dc.contributor.authorZhang, Zhongyi
dc.date.accessioned2022-03-24T13:31:36Z
dc.date.available2022-03-24T13:31:36Z
dc.date.issued2022
dc.description.abstractWe describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims at detecting signals of new physics at the 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−110 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.
dc.identifier.citationAarrestad, Thea, van Beekveld, Melissa, Bona, Marcella, et al.. "The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider." <i>SciPost Physics,</i> 12, (2022) SciPost Foundation: https://doi.org/10.21468/SciPostPhys.12.1.043.
dc.identifier.digitalSciPostPhys_12_1_043
dc.identifier.doihttps://doi.org/10.21468/SciPostPhys.12.1.043
dc.identifier.urihttps://hdl.handle.net/1911/112034
dc.language.isoeng
dc.publisherSciPost Foundation
dc.rightsThis work is licensed under the Creative Commons Attribution 4.0 International License.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleThe Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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