The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
| Autor: | Thea Aarrestad, Melissa van Beekveld, Marcella Bona, Antonio Boveia, Sascha Caron, Joe Davies, Andrea de Simone, Caterina Doglioni, Javier Duarte, Amir Farbin, Honey Gupta, Luc Hendriks, Lukas A. Heinrich, James Howarth, Pratik Jawahar, Adil Jueid, Jessica Lastow, Adam Leinweber, Judita Mamuzic, Erzsébet Merényi, Alessandro Morandini, Polina Moskvitina, Clara Nellist, Jennifer Ngadiuba, Bryan Ostdiek, Maurizio Pierini, Baptiste Ravina, Roberto Ruiz de Austri, Sezen Sekmen, Mary Touranakou, Marija Vaškeviciute, Ricardo Vilalta, Jean-Roch Vlimant, Rob Verheyen, Martin White, Eric Wulff, Erik Wallin, Kinga A. Wozniak, Zhongyi Zhang |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
QC1-999 Other Fields of Physics FOS: Physical sciences General Physics and Astronomy Machine Learning (stat.ML) 01 natural sciences High Energy Physics - Experiment physics.data-an High Energy Physics - Experiment (hep-ex) High Energy Physics - Phenomenology (hep-ph) Statistics - Machine Learning 0103 physical sciences ddc:530 010306 general physics Mathematical Physics and Mathematics Particle Physics - Phenomenology 010308 nuclear & particles physics hep-ex Physics hep-ph stat.ML High Energy Physics - Phenomenology Physics - Data Analysis Statistics and Probability Data Analysis Statistics and Probability (physics.data-an) Particle Physics - Experiment |
| Zdroj: | SciPost Physics, Vol 12, Iss 1, p 043 (2022) SciPost physics 12(1), 043 (2022). doi:10.21468/SciPostPhys.12.1.043 |
| Popis: | 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 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. v1: 54 pages, 24 figures. v2: 56 pages, citations added, extend discussion of look-elsewhere-effect, results unchanged; v3. minor typos and updated references |
| Databáze: | OpenAIRE |
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