Speaker
Edward Shields
(Weizmann Institute of Science (IL))
Description
Machine Learning methods are becoming more prevalent in physics for a variety of tasks, and some have already been demonstrated within the directional recoil detection community for reconstruction and direction prediction tasks. We will propose a modern architecture to predict direction and class of recoils in one model. Further, we will propose a generative ML model, based on a super-resolution technique, to enhance detector readout beyond the physical readout of a TPC. If proven to be viable, this technique could be incorporated into detector designs, to allow for greater readout granularity beyond financial and technical constraints.