Fast and flexible analysis of direct dark matter search data with machine learning

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Abstract

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 nonlinear 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 scalability furthermore enables searching for dark matter using additional variables without significant computational burden. We demonstrate this by including a light signal pulse shape variable alongside more traditional inputs, such as light and charge signal strengths. This technique can be exploited by future dark matter experiments to make use of additional information, reduce computational resources needed for signal searches and simulations, and make inclusion of physical nuisance parameters in fits tractable.

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Item Type:	Article
Authors/Creators:	Akerib, DS Alsum, S Araújo, HM Bai, X Balajthy, J Bang, J Baxter, A Bernard, EP Bernstein, A Biesiadzinski, TP Boulton, EM Boxer, B Brás, P Burdin, S Byram, D Carrara, N Carmona-Benitez, MC Chan, C Cutter, JE de Viveiros, L Druszkiewicz, E Ernst, J Fan, A Fiorucci, S Gaitskell, RJ Ghag, C Gilchriese, MGD Gwilliam, C Hall, CR Haselschwardt, SJ Hertel, SA Hogan, DP Horn, M Huang, DQ Ignarra, CM Jacobsen, RG Jahangir, O Ji, W Kamdin, K Kazkaz, K Khaitan, D Korolkova, EV Kravitz, S Kudryavtsev, VA https://orcid.org/0000-0002-7018-5827 Leason, E Lenardo, BG Lesko, KT Liao, J Lin, J Lindote, A Lopes, MI Manalaysay, A Mannino, RL Marangou, N McKinsey, DN Mei, D-M Morad, JA Murphy, ASJ Naylor, A Nehrkorn, C Nelson, HN Neves, F Nilima, A Oliver-Mallory, KC Palladino, KJ Rhyne, C Riffard, Q Rischbieter, GRC Rossiter, P Shaw, S Shutt, TA Silva, C Solmaz, M Solovov, VN Sorensen, P Sumner, TJ Swanson, N Szydagis, M Taylor, DJ Taylor, R Taylor, WC Tennyson, BP Terman, PA Tiedt, DR To, WH Tvrznikova, L Utku, U Vacheret, A Vaitkus, A Velan, V Webb, RC White, JT Whitis, TJ Witherell, MS Wolfs, FLH Woodward, D Xian, X Xu, J Zhang, C
Copyright, Publisher and Additional Information:	© 2022 American Physical Society. This is an author-produced version of a paper subsequently published in Physical Review D. Uploaded in accordance with the publisher's self-archiving policy.
Dates:	Published: 1 October 2022 Published (online): 28 October 2022 Accepted: 13 October 2022
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Science (Sheffield) > Department of Physics and Astronomy (Sheffield)
Depositing User:	Symplectic Sheffield
Date Deposited:	01 Nov 2022 13:57
Last Modified:	01 Nov 2022 13:57
Status:	Published
Publisher:	American Physical Society (APS)
Refereed:	Yes
Identification Number:	10.1103/physrevd.106.072009
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:192854

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Fast and flexible analysis of direct dark matter search data with machine learning. (deposited 01 Nov 2022 13:47)
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Fast and flexible analysis of direct dark matter search data with machine learning

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