EPJ Web Conf.
Volume 251, 202125th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021)
|Number of page(s)||11|
|Published online||23 August 2021|
Event vertex reconstruction with deep neural networks for the DarkSide-20k experiment
1 Department of Physics and Astronomy, University of Hawaii at Manoa, Honolulu, USA
Published online: 23 August 2021
While deep learning techniques are becoming increasingly more popular in high-energy and, since recently, neutrino experiments, they are less confidently used in direct dark matter searches based on dual-phase noble gas TPCs optimized for low-energy signals from particle interactions.
In the present study, the application of modern deep learning methods for event vertex reconstruction is demonstrated with an example of the 50-tonne liquid argon DarkSide-20k TPC with 8200 photosensors.
The developed methods successfully reconstruct event positions within sub-cm precision and apply to any dual-phase argon or xenon TPC of arbitrary size with any sensor shape and array pattern.
© The Authors, published by EDP Sciences, 2021
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