Speaker
Victor Goicoechea Casanueva
(University of Hawai'i at Manoa (US))
Description
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, application of modern deep learning methods for event ver- tex reconstruction is demonstrated with an example of the 50-tonne liquid argon DarkSide-20k TPC with almost 10 thousand photosensors.
The developed methods successfully reconstruct event’s position withing sub- cm precision and are applicable to any dual-phase argon or xenon TPC of arbi- trary size with any sensor shape and array pattern.
Primary authors
Victor Goicoechea Casanueva
(University of Hawai'i at Manoa (US))
Dr
Alexander Kish
(University of Hawaii at Manoa)
Prof.
Jelena Maricic
(University of Hawaii at Maoa)
