Speaker
Nicola Fulvio Calabria
(Istituto Nazionale di Fisica Nucleare)
Description
Super-Kamiokande is a 50-kton Water Cherenkov detector, operating since 1996 in the Kamioka mine, Japan, whose broad scientific program spans from neutrino physics to baryon number violating processes, such as proton decay. In this preliminary study I show the development of a Deep Learning model, based on Convolutional Neural Networks (CNN) and Residual Neural Networks (ResNet), for event reconstruction in Super-Kamiokande. To do so, simulated event samples have been used. This study aims to the development of a software tool to be employed alongside the official reconstruction software (fiTQun) to improve particle detection and reconstruction for proton decay analysis.
| AI keywords | CNN;ResNet;pattern recognition;image analysis |
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Primary author
Nicola Fulvio Calabria
(Istituto Nazionale di Fisica Nucleare)
