{"paper":{"title":"Generative Adversarial Networks for Scintillation Signal Simulation in EXO-200","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","physics.ins-det"],"primary_cat":"hep-ex","authors_text":"A. Craycraft, A. Der Mesrobian-Kabakian, A. Dolgolenko, A. Iverson, A. Jamil, A. Johnson, A. Karelin, A.K. Soma, A. Kuchenkov, A. Larson, A.L. Robinson, A. Odian, A. Perna, A. Piepke, A. Pocar, B. Cleveland, B.G. Lenardo, B. Mong, C. Chambers, C. Hall, C. Jessiman, C. Licciardi, D. Beck, D.C. Moore, D. Fairbank, D. Fudenberg, D. Sinclair, D.S. Leonard, E.V. Hansen, F. Reti\\`ere, G. Anton, G.F. Cao, G. Gratta, G.S. Li, I. Badhrees, I. Ostrovskiy, J. Davis, J. Dilling, J. Echevers, J. Farine, J. Hoessl, J. Runge, J. Todd, K. Murray, K. Skarpaas, K.S. Kumar, L. Darroch, L.J. Kaufman, L. Yang, M. Breidenbach, M. Coon, M. Hughes, M.J. Dolinski, M.J. Jewell, M. Tarka, O. Njoya, O. Nusair, P.C. Rowson, P. Fierlinger, P. Gautam, P. Hufschmidt, P.S. Barbeau, R. DeVoe, R. Gornea, R. Kr\\\"ucken, R. MacLellan, R. Tsang, S. Al Kharusi, S. Delaquis, S. Feyzbakhsh, S.J. Daugherty, S. Li, S. Schmidt, S. Thibado, T. Bhatta, T. Brunner, T. Daniels, T.I. Totev, T. Koffas, T. McElroy, T. Michel, T. Tolba, V. Belov, V. Stekhanov, W. Fairbank Jr., W.R. Cen, Y.H. Lin, Y. Lan, Y.S. Fu, Z. Li","submitted_at":"2023-03-11T05:16:20Z","abstract_excerpt":"Generative Adversarial Networks trained on samples of simulated or actual events have been proposed as a way of generating large simulated datasets at a reduced computational cost. In this work, a novel approach to perform the simulation of photodetector signals from the time projection chamber of the EXO-200 experiment is demonstrated. The method is based on a Wasserstein Generative Adversarial Network - a deep learning technique allowing for implicit non-parametric estimation of the population distribution for a given set of objects. Our network is trained on real calibration data using raw "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.06311","kind":"arxiv","version":2},"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/2303.06311/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"}