Machine Learning Applications for the NEWS-G Dark Matter Search Experiment
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Authors
Rowe, Noah
Date
Type
thesis
Language
eng
Keyword
Dark Matter , Particle Physics , Machine Learning , Neural Networks , Spherical Proportional Counters , Autoencoders
Alternative Title
Abstract
The NEWS-G collaboration searches for light dark matter using Spherical Proportional Counter (SPC) detectors in low-background environments. Currently, the collaboration is working to publish results from data collected in 2019 and is commissioning a new experiment at SNOLAB.
This thesis outlines an avenue to incorporate machine learning techniques within the NEWS-G analysis process. Two neural network architectures are designed and tested: a deep learning convolutional autoencoder for removing electronic noise from recorded detector signals, and a modified single output version of the former to predict pulse-specific physics-based characteristics. The noise-removing model is found to be statistically more effective than traditional noise-removal methods, and both models provide benefits for energy measurements, primary electron counting, and event identification. Limit calculations were performed for a hypothetical dark matter search experiment with and without each model for event identification, resulting in more restrictive limits when machine learning was incorporated.
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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution-NonCommercial 4.0 International
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution-NonCommercial 4.0 International