Speaker
Cristovao Vilela
(Stony Brook University)
Description
Deep neural networks are an area of very active research in neutrino event reconstruction. On the other hand, state-of-the-art reconstruction methods for water Cherenkov detectors use more traditional maximum-likelihood approaches. Here we present initial studies for a convolutional neural network that generates probability density functions for the data (hit charge and time) observed at each photosensor in water Cherenkov detectors. Such a neural network can be used to incorporate high-performance deep learning methods in existing maximum-likelihood reconstruction algorithms. We will discuss merits of this approach, its current status and future plans, such as the development of bespoke loss functions and adversarial training.
Primary author
Cristovao Vilela
(Stony Brook University)
