Machine Learning Approach for Event Position Reconstruction in the DEAP-3600 Dark Matter Search Experiment

Abstract

In addition to classical analytical data processing methods, machine learning methods are widely used for data analysis in elementary particle physics. Most often, such techniques are used to identify a particular class of events (the classification problem) or to predict a certain event parameter (the regression problem). Here, we present the result of using a machine learning model to solve the regression problem of event position reconstruction in the DEAP-3600 dark matter search detector. A neural network was used as a machine learning model. Improving the position resolution will improve the reduction in background events, while increasing the signal acceptance for weakly interacting massive particles.

Keywords

• dark matter
• liquid argon detector
• position reconstruction
• machine learning
• neural network