On-Detector Machine Learning for Beam-Induced Background Rejection at a 10 TeV Muon Collider
This paper demonstrates that lightweight neural networks implemented directly on the vertex detector can effectively reject 88–90% of beam-induced background while maintaining 99% signal efficiency, offering a viable hardware-compatible strategy for managing readout constraints at a future 10 TeV Muon Collider.