Machine Learning in Direct Dark Matter Experiments
Lead Research Organisation:
University College London
Department Name: Physics and Astronomy
Abstract
Direct Dark Matter search experiments operate highly sensitive detectors in deep underground laboratories, seeking to detect rare and low-energy scatters from Dark Matter particles in our galaxy. The world-leading LUX experiment, based at the Sanford Underground Research Facility, S. Dakota, operates a xenon target that is the most radio-quiet environment on Earth in the hunt for Weakly Interacting Massive Particles (WIMPs). WIMPs are expected to produce characteristic single vertex elastic scattering signatures. The rate of candidate events that satisfy this requirement is low, about 2 per day, and this is consistent with background expectation. However, there are many WIMP and non-WIMP models of Dark Matter that may produce significantly different signatures. LUX triggers about 1 million times per day to record data from completely uncharted electroweak parameter space, potentially containing new physics and non-standard WIMP or non-WIMP Dark Matter signals. The techniques of Machine Learning and Deep Learning present opportunities to analyse this data efficiently, particularly where faint signals with unknown characteristics may be hidden amongst large backgrounds. Developing these techniques could prove to be the key for discovery in the next generation of leading Dark Matter experiment, LZ, presently under construction and set to begin taking data within the lifetime of this project. LZ will examine the bulk of the favoured theoretical parameter space for Dark Matter, uncovering unknown backgrounds never previously encountered and potential signals. This project will develop the routines to analyse the existing rich LUX data for any galactic signals or new backgrounds, and prepare the framework for a robust and rapid interpretation of the data from LZ, be it for the first discovery of WIMPs or any hints of physics Beyond the Standard Model.
Publications
Akerib D
(2020)
Improved modeling of ß electronic recoils in liquid xenon using LUX calibration data
in Journal of Instrumentation
Akerib D
(2020)
The LUX-ZEPLIN (LZ) experiment
in Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Akerib D
(2020)
Extending light WIMP searches to single scintillation photons in LUX
in Physical Review D
Akerib D
(2019)
Improved measurements of the ß -decay response of liquid xenon with the LUX detector
in Physical Review D
Akerib D
(2020)
First direct detection constraint on mirror dark matter kinetic mixing using LUX 2013 data
in Physical Review D
Akerib DS
(2019)
Results of a Search for Sub-GeV Dark Matter Using 2013 LUX Data.
in Physical review letters
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
ST/P006736/1 | 30/09/2017 | 29/09/2024 | |||
1966428 | Studentship | ST/P006736/1 | 30/09/2017 | 29/09/2021 | Omar Jahangir |