@article{Akerib:2022:10.1103/PhysRevD.106.072009,
author = {Akerib, DS and Alsum, S and Araújo, HM and Bai, X and Balajthy, J and Bang, J and Baxter, A and Bernard, EP and Bernstein, A and Biesiadzinski, TP and Boulton, EM and Boxer, B and Brás, P and Burdin, S and Byram, D and Carrara, N and Carmona-Benitez, MC and Chan, C and Cutter, JE and De, Viveiros L and Druszkiewicz, E and Ernst, J and Fan, A and Fiorucci, S and Gaitskell, RJ and Ghag, C and Gilchriese, MGD and Gwilliam, C and Hall, CR and Haselschwardt, SJ and Hertel, SA and Hogan, DP and Horn, M and Huang, DQ and Ignarra, CM and Jacobsen, RG and Jahangir, O and Ji, W and Kamdin, K and Kazkaz, K and Khaitan, D and Korolkova, EV and Kravitz, S and Kudryavtsev, VA and Leason, E and Lenardo, BG and Lesko, KT and Liao, J and Lin, J and Lindote, A and Lopes, MI and Manalaysay, A and Mannino, RL and Marangou, N and McKinsey, DN and Mei, DM and Morad, JA and Murphy, ASJ and Naylor, A and Nehrkorn, C and Nelson, HN and Neves, F and Nilima, A and Oliver-Mallory, KC and Palladino, KJ and Rhyne, },
doi = {10.1103/PhysRevD.106.072009},
journal = {Physical Review D},
title = {Fast and flexible analysis of direct dark matter search data with machine learning},
url = {http://dx.doi.org/10.1103/PhysRevD.106.072009},
volume = {106},
year = {2022}
}

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TY  - JOUR
AB  - 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.
AU  - Akerib,DS
AU  - Alsum,S
AU  - Araújo,HM
AU  - Bai,X
AU  - Balajthy,J
AU  - Bang,J
AU  - Baxter,A
AU  - Bernard,EP
AU  - Bernstein,A
AU  - Biesiadzinski,TP
AU  - Boulton,EM
AU  - Boxer,B
AU  - Brás,P
AU  - Burdin,S
AU  - Byram,D
AU  - Carrara,N
AU  - Carmona-Benitez,MC
AU  - Chan,C
AU  - Cutter,JE
AU  - De,Viveiros L
AU  - Druszkiewicz,E
AU  - Ernst,J
AU  - Fan,A
AU  - Fiorucci,S
AU  - Gaitskell,RJ
AU  - Ghag,C
AU  - Gilchriese,MGD
AU  - Gwilliam,C
AU  - Hall,CR
AU  - Haselschwardt,SJ
AU  - Hertel,SA
AU  - Hogan,DP
AU  - Horn,M
AU  - Huang,DQ
AU  - Ignarra,CM
AU  - Jacobsen,RG
AU  - Jahangir,O
AU  - Ji,W
AU  - Kamdin,K
AU  - Kazkaz,K
AU  - Khaitan,D
AU  - Korolkova,EV
AU  - Kravitz,S
AU  - Kudryavtsev,VA
AU  - Leason,E
AU  - Lenardo,BG
AU  - Lesko,KT
AU  - Liao,J
AU  - Lin,J
AU  - Lindote,A
AU  - Lopes,MI
AU  - Manalaysay,A
AU  - Mannino,RL
AU  - Marangou,N
AU  - McKinsey,DN
AU  - Mei,DM
AU  - Morad,JA
AU  - Murphy,ASJ
AU  - Naylor,A
AU  - Nehrkorn,C
AU  - Nelson,HN
AU  - Neves,F
AU  - Nilima,A
AU  - Oliver-Mallory,KC
AU  - Palladino,KJ
AU  - Rhyne,C
AU  - Riffard,Q
AU  - Rischbieter,GRC
AU  - Rossiter,P
AU  - Shaw,S
AU  - Shutt,TA
AU  - Silva,C
AU  - Solmaz,M
AU  - Solovov,VN
AU  - Sorensen,P
AU  - Sumner,TJ
AU  - Swanson,N
AU  - Szydagis,M
AU  - Taylor,DJ
AU  - Taylor,R
AU  - Taylor,WC
AU  - Tennyson,BP
AU  - Terman,PA
AU  - Tiedt,DR
AU  - To,WH
AU  - Tvrznikova,L
AU  - Utku,U
AU  - Vacheret,A
AU  - Vaitkus,A
AU  - Velan,V
AU  - Webb,RC
AU  - White,JT
AU  - Whitis,TJ
AU  - Witherell,MS
AU  - Wolfs,FLH
AU  - Woodward,D
AU  - Xian,X
AU  - Xu,J
AU  - Zhang,C
DO  - 10.1103/PhysRevD.106.072009
PY  - 2022///
SN  - 2470-0010
TI  - Fast and flexible analysis of direct dark matter search data with machine learning
T2  - Physical Review D
UR  - http://dx.doi.org/10.1103/PhysRevD.106.072009
VL  - 106
ER  - 

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