Open AccessMachine learning approach to pulse shape discrimination in liquid noble gas detectors
Author(s) -
A. Grobov
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1390/1/012110
Subject(s) - detector , artificial neural network , classifier (uml) , computer science , artificial intelligence , task (project management) , noble gas , pattern recognition (psychology) , machine learning , physics , engineering , telecommunications , systems engineering , quantum mechanics
Study of the event classification in liquid noble gas detectors is presented. The discrimination between different events usually done by prompt light fraction. To tackle this task we adopt the neural net classifier and use pulse shape as an input feature. The main difficulty comes from low-energy events that are difficult to separate. This is important because these events provide a background for dark matter searches. We find that neural networks are suitable for this task.