10 TeV缪子对撞机中用于束流诱导本底抑制的探测器上机器学习方法
On-Detector Machine Learning for Beam-Induced Background Rejection at a 10 TeV Muon Collider
AI总结：
针对10 TeV缪子对撞机的束流诱导本底问题，研究探测器上的轻量级机器学习架构，可在99%信号效率下实现88%-90%的数据缩减，为满足其径迹探测器读出要求提供可行方案。
AI中文摘要：
10 TeV缪子对撞机是未来能量前沿装置的极具吸引力的候选者，为探索粒子物理基本规律提供了前所未有的机会。对撞机环中的缪子衰变会产生强烈的束流诱导本底（BIB），可能使探测器 occupancy 过载并超出读出带宽约束。我们研究了探测器上机器学习在顶点探测器中用于BIB抑制的潜力，利用像素簇形状区分本底与碰撞产物。我们研究了三类轻量级神经网络架构，并使用高级综合评估其实现可行性。所选架构在99%信号效率下实现了88%至90%的数据缩减，同时所需硬件资源与潜在的ASIC实现兼容。这些结果证明了直接在像素读出中进行大量BIB抑制的潜力，为满足未来缪子对撞机的径迹探测器读出要求提供了一种策略。
英文摘要：
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.
