UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

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

Akerib, DS; Alsum, S; Araújo, HM; Bai, X; Balajthy, J; Bang, J; Baxter, A; ... Zhang, C; + view all (2022) Fast and flexible analysis of direct dark matter search data with machine learning. Physical Review D , 106 (7) , Article 072009. 10.1103/PhysRevD.106.072009. Green open access

[thumbnail of PhysRevD.106.072009.pdf] Text
PhysRevD.106.072009.pdf - Published Version

Download (12MB)

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.

Type: Article
Title: Fast and flexible analysis of direct dark matter search data with machine learning
Open access status: An open access version is available from UCL Discovery
DOI: 10.1103/PhysRevD.106.072009
Publisher version: https://doi.org/10.1103/PhysRevD.106.072009
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy
URI: https://discovery.ucl.ac.uk/id/eprint/10160299
Downloads since deposit
4Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item