Boosted decision trees approach to neck alpha events discrimination in DEAP-3600 experiment

Machine learning (ML) has been widely applied in high energy physics to help the physical community in particle classification and data analysis. Here we describe the application of machine learning to solve the problem of classifying background and signal events for the DEAP-3600 dark matter search...

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Bibliographic Details
Published in:Physica scripta 2020-07, Vol.95 (7), p.74007
Main Authors: Grobov, A, Ilyasov, A
Format: Article
Language:English
Subjects:
DEAP-3600
event classification
liquid noble gases detector
machine learning
WIMP
XGBoost
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Summary:Machine learning (ML) has been widely applied in high energy physics to help the physical community in particle classification and data analysis. Here we describe the application of machine learning to solve the problem of classifying background and signal events for the DEAP-3600 dark matter search experiment (SNOLAB, Canada). We apply Boosted Decision Trees (BDT) algorithm of ML with improvements from Extra Trees and eXtra Gradient Boosting (XGBoost) methods [1, 2].
ISSN:0031-8949
1402-4896
DOI:10.1088/1402-4896/ab8dff