{"paper":{"title":"Fast and Flexible Analysis of Direct Dark Matter Search Data with Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM","hep-ex","physics.ins-det"],"primary_cat":"astro-ph.CO","authors_text":"A. Baxter, A. Bernstein, A. Fan, A. Lindote, A. Manalaysay, A. Naylor, A. Nilima, A.St.J. Murphy, A. Vacheret, A. Vaitkus, B. Boxer, B.G. Lenardo, B.P. Tennyson, C. Chan, C. Ghag, C. Gwilliam, C.M. Ignarra, C. Nehrkorn, C.R. Hall, C. Rhyne, C. Silva, C. Zhang, D. Byram, D.J. Taylor, D. Khaitan, D.-M. Mei, D.N. McKinsey, D.P. Hogan, D.Q. Huang, D.R. Tiedt, D. Woodward, E. Druszkiewicz, E. Leason, E.M. Boulton, E.P. Bernard, E.V. Korolkova, F.L.H. Wolfs, F. Neves, G.R.C. Rischbieter, H.M. Ara\\'ujo, H.N. Nelson, J.A. Morad, J. Balajthy, J. Bang, J.E. Cutter, J. Ernst, J. Liao, J. Lin, J.T. White, J. Xu, K.C. Oliver-Mallory, K.J. Palladino, K. Kamdin, K. Kazkaz, K.T. Lesko, L. de Viveiros, L. Tvrznikova, LUX Collaboration: D.S. Akerib, M.C. Carmona-Benitez, M.G.D. Gilchriese, M. Horn, M.I. Lopes, M. Solmaz, M.S. Witherell, M. Szydagis, N. Carrara, N. Marangou, N. Swanson, O. Jahangir, P.A. Terman, P. Br\\'as, P. Rossiter, P. Sorensen, Q. Riffard, R.C. Webb, R.G. Jacobsen, R.J. Gaitskell, R.L. Mannino, R. Taylor, S.A. Hertel, S. Alsum, S. Burdin, S. Fiorucci, S.J. Haselschwardt, S. Kravitz, S. Shaw, T.A. Shutt, T.J. Sumner, T.J. Whitis, T.P. Biesiadzinski, U. Utku, V.A. Kudryavtsev, V.N. Solovov, V. Velan, W.C. Taylor, W.H. To, W. Ji, X. Bai, X. Xian","submitted_at":"2022-01-15T02:12:47Z","abstract_excerpt":"We present the results from combining machine learning with the profile likelihood fit procedure, using data from the Large Underground Xenon (LUX) dark matter experiment. This approach demonstrates reduction in computation time by a factor of 30 when compared with the previous approach, without loss of performance on real data. We establish its flexibility to capture non-linear correlations between variables (such as smearing in light and charge signals due to position variation) by achieving equal performance using pulse areas with and without position-corrections applied. Its efficiency and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.05734","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/2201.05734/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"}