Boosted decision trees approach to neck alpha events discrimination in DEAP-3600 experiment
Instrumentation and Detectors
2020-09-03 v1 Instrumentation and Methods for Astrophysics
High Energy Physics - Experiment
Abstract
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.
Cite
@article{arxiv.2009.00895,
title = {Boosted decision trees approach to neck alpha events discrimination in DEAP-3600 experiment},
author = {Alexey Grobov and Aidar Ilyasov},
journal= {arXiv preprint arXiv:2009.00895},
year = {2020}
}
Comments
7 pages, 5 figures, 1 table