https://doi.org/10.1051/epjconf/202125103029
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
* e-mail: victorgc@hawaii.edu
** e-mail: akish@phys.hawaii.edu
*** e-mail: jelena@phys.hawaii.edu
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
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
