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Published January 28, 2022 | Published + Accepted Version
Journal Article Open

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


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 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.

Additional Information

© 2022 T. Aarrestad et al. This work is licensed under the Creative Commons Attribution 4.0 International License. Published by the SciPost Foundation. Received 21-06-2021. Accepted 23-12-2021. Published 28-01-2022. MvB acknowledges support from the Science and Technology Facilities Council (grant number ST/T000864/1). The work of A.J. is supported by the National Research Foundation of Korea, Grant No. NRF-2019R1A2C1009419. The work of J.M. is supported in part by the Generalitat Valenciana (GV) through the contract APOSTD/2019/165, and by Spanish and European funds under the project PGC2018-094856-B-I00 (MCIU/AEI/FEDER, EU). The work of R.RdA. and J.M. is supported by the Artemisa project, co-funded by the European Union through the 2014-2020 FEDER Operative Programme of Comunitat Valenciana, project IDIFEDER/2018/048. The work of J.H. and B.R. is supported by the Royal Society under grant URF\R1\191524. The work of C.D. is supported by the Swedish Research Council. Research by A.B. is supported by the US Department of Energy under contract DE-SC0011726. The work of B.O. is supported by the U.S. Department of Energy under contract DE-SC0013607. This work is supported by the National Science Foundation under Cooperative Agreement PHY-2019786 (The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, http://iaifi.org/). The work of P.J. was supported by the DIANA-HEP Graduate Fellowship program and the ThinkSwiss Research Scholarship. P.J. M.P., M.T., and K.A.W are supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement no 772369. J.M.D. is supported by the DOE, Office of Science, Office of High Energy Physics Early Career Research program under Award No. DE-SC0021187 and by the DOE, Office of Advanced Scientific Computing Research under Award No. DE-SC0021396 (FAIR4HEP). J-R. V. is supported by the U.S. DOE, Office of Science, Office of High Energy Physics under Award No. DE-SC0011925, DE-SC0019227, and DE-AC02-07CH11359. Computations in this paper were run on the FASRC Cannon cluster supported by the FAS Division of Science Research Computing Group at Harvard University. M.W. and A.L. are supported by the Australian Research Council Discovery Project DP180102209 and the ARC Centre of Excellence in Dark Matter Particle Physics (CE200100008). RV acknowledges support from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement No. 788223, PanScales), and from the Science and Technology Facilities Council (STFC) under the grant ST/P000274/1.

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Published - SciPostPhys_12_1_043.pdf

Accepted Version - 2105.14027.pdf


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August 22, 2023
October 23, 2023