Speaker
Description
This contribution introduces a new ML framework designed to distinguish between signal and background in a high-energy physics experiment. Employing a dataset processed through the Constrained Flood Fill Algorithm, this framework utilizes a multimodal approach which integrates modified ResNet models via a late fusion technique and implements a gating mechanism for each readout plane. Promising performance in signal discrimination by the late fusion model has been demonstrated, showing favorable comparisons with previous methods where preselection cuts were combined with Boosted Decision Trees (BDT), including features derived from a CNN. A principal advantage of the presented ML framework is its capacity to directly analyze raw detector outputs, eliminating the necessity for track reconstruction. This approach effectively tackles the challenge of incomplete association of particles with reconstructed tracks and the issue of background events being mis-reconstructed as signal events.