{"paper":{"title":"The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["hep-ex","physics.data-an","stat.ML"],"primary_cat":"hep-ph","authors_text":"A. Boveia, A. De Simone, A. Farbin, A. Jueid, A. Leinweber, A. Morandini, B. Ostdiek, B. Ravina, C. Doglioni, C. Nellist, E. Mer\\'enyi, E. Wallin, E. Wulff, H. Gupta, J. Davies, J. Howarth, J. Lastow, J. Mamuzic, J.M. Duarte, J. Ngadiuba, J.R. Vlimant, K.A. Wozniak, L. Heinrich, L. Hendriks, M. Bona, M. Pierini, M. Touranakou, M. van Beekveld, M. Va\\v{s}kevi\\v{c}i\\=ute, M. White, P. Jawahar, P. Moskvitina, R. Ruiz de Austri, R. Verheyen, R. Vilalta, S. Caron, S. Sekmen, T. Aarrestad, Z. Zhang","submitted_at":"2021-05-28T18:00:02Z","abstract_excerpt":"We 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 $10~\\rm{fb}^{-1}$ of proton-proton collisions at a center-of-mass energy of 13 TeV. We then rev"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.14027","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2105.14027/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}