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- Title
Machine Learning Approach for Event Position Reconstruction in the DEAP-3600 Dark Matter Search Experiment.
- Authors
Collaboration, DEAP
- 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.
- Subjects
MACHINE learning; DARK matter; WEAKLY interacting massive particles; PARTICLE physics
- Publication
Physics (2624-8174), 2023, Vol 5, Issue 2, p483
- ISSN
2624-8174
- Publication type
Article
- DOI
10.3390/physics5020033