Speaker
Description
Muon reconstruction in high-energy physics experiments relies on identifying detector hits originating from muons and fitting trajectories through them to extract track parameters. This process currently consists of separating signal hits from background before the track reconstruction can be performed. In this work, we present a deep learning solution for discriminating between background and signal hits which could be used for a muon trigger.
Specifically, we propose a compact Transformer encoder architecture to act as a binary classifier that filters out irrelevant background. We investigate the added benefit of a distance-based attention mask for inserting a locality prior, and of the novel "location encoding" that enables the model to learn the detector geometry. The proposed architecture is evaluated on a toy sample simulating muon drift tube chambers, for which we report high background rejection and signal efficiency rates.