Machine learning approach to neck alpha events discrimination in DEAP-3600 experiment

Oct 14, 2020, 6:10 PM


Poster report Section 5. Neutrino physics and astrophysics. Poster session 5


Mr Aidar Ilyasov (NRNU MEPhI & NRC KI)


Machine Learning (ML) have been widely applied in the High Energy Physics (HEP) to help physical community to solve complex problem in classification and analysis. Here we describe application of ML to solve the problem of classification background and signal events in DEAP-3600 experiment (SNOLAB, Canada) that constructed to search WIMP particles. We apply Boosted Decision Trees (BDT) method of ML, which upgraded by using Extra Trees and eXtra Gradient boosting method (XGBoost).

Primary author

Mr Aidar Ilyasov (NRNU MEPhI & NRC KI)


Dr Alexey Grobov (NRNU MEPhI & NRC KI)

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