On-Detector Machine Learning for Beam-Induced Background Rejection at a 10 TeV Muon Collider
Daniel Abadjiev, Eliza Howard, Tsz Ngong You, Ryan Michaud, Benjamin Ryan Roberts, Benjamin Rosser, Karri Folan Di Petrillo, Doug Berry, Arghya Ranjan Das, Jennet Dickinson, Giuseppe Di Guglielmo, Harshul Gupta, Farah Fahim, Abhijith Gandrakota, Lindsey Gray, James Hirschauer, David Jiang, Shiqi Kuang, Ron Lipton, Mira Littmann, Miaoyuan Liu, Nicholas Manganelli, Petar Maksimovic, Corrinne Mills, Mark S. Neubauer, Aidan Nicholas, Benjamin Parpillon, Jannicke Pearkes, Adam Quinn, Danush Shekar, Ricardo Silvestre, Chinar Syal, Morris Swartz, Nhan Tran, Amit Trivedi, Keith Ulmer, Mohammad Abrar Wadud, Benjamin Weiss
Abstract
A 10 TeV Muon Collider is a compelling candidate for a future energy-frontier facility, offering unprecedented opportunities to explore the fundamental laws of particle physics. Muon decays in the collider ring produce intense beam-induced background (BIB) that can overwhelm detector occupancy and exceed readout bandwidth constraints. We investigate the potential of on-detector Machine Learning for BIB rejection in the vertex detector, exploiting pixel cluster shapes to distinguish background from collision products. We study three classes of lightweight neural-network architectures, and evaluate their implementation feasibility using high-level synthesis. Selected architectures achieve 88 to 90% data reduction at 99% signal efficiency, while requiring hardware resources compatible with potential ASIC implementation. These results demonstrate the potential of performing substantial BIB rejection directly in the pixel readout, providing a strategy for meeting the tracker readout requirements at a future Muon Collider.
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