On-detector machine learning for beam-induced background rejection at a 10 TeV muon collider
Year: 2026
Type: article
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 88-90% 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.
Authors: Daniel Svetlin Abadjiev, Eliza Howard, Tsz N You, Ryan Michaud, Benjamin Ryan Roberts, Benjamin Rosser, Karri Folan Di Petrillo, Douglas Berry, Arghya Ranjan Das, Jennet Dickinson, Giuseppe D Guglielmo, Harshul Gupta, Farah Khalid Fahim, Abhijith Gandrakota, Lindsey Gray, James Hirschauer, David Jiang, Shiqi Kuang, Ronald Lipton, Mira Littmann, Miaoyuan Liu, Nicholas Manganelli, Petar Maksimovic, Corrinne Mills, Mark Neubauer, Aidan Nicholas, Benjamin Parpillon, Jannicke Pearkes, Adam Quinn, Danush Shekar, Ricardo Silvestre, Chinar Syal, Morris Swartz, Nhan Tran, Amit Ranjan Trivedi, Keith Ulmer, Mohammad A Wadud, Benjamin Weiss
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,000
Awards: G1480457092, Studies in Elementary Particle Physics, Collaborative Proposal: Geodynamic Tomography: A New Approach to Studies of Mantle Upwelling and its Application to the Lau Basin and The East Pacific Rise, CAREER: Pixel Powered Discoveries at the LHC and Beyond, G1959365839, G2376186588, G2388497102, Bridge to the HL-LHC: CMS Upgrades and Run 3 Physics, G3270322552, G5574724152, G6471802762, HDR Institute: Accelerated AI Algorithms for Data-Driven Discovery, G6671297155
OpenAlex ID: W7204121315