Speaker
Description
Long-lived particles decaying within the CMS muon system can deposit dense showers of hits in the endcap Cathode Strip Chambers (CSCs), called muon detector showers (MDS), whose hit multiplicity is the primary handle for their identification. Standard CSC reconstruction, built for isolated muon tracks, breaks down here: overlapping detector signals inflate and smear the hit count where the shower is densest. We present a geometry-aware cross-attention transformer that reconstructs shower hits directly from the raw CSC readout, trained on Geant4 truth hits by set prediction. It recovers the true hit multiplicity and cluster shape where classical reconstruction saturates, extending CSC hit reconstruction to dense showers that standard algorithms cannot handle.