粒子碰撞事件的紧凑表示用于物理引导的机器学习
Compact Representation of Particle-Collision Events for Physics-Informed Machine Learning
AI总结：
RMM-C46通过聚合不变质量、快速性差值和横向能量成分，将高维RMM压缩为低维可解释特征，提升物理引导机器学习和量子计算的效率与兼容性。
摘要：
我们介绍了一种紧凑的、受物理驱动的事件表示方法RMM-C46，旨在将高维的快速性质量矩阵（RMM）压缩成低维、可解释的特征集，适用于物理引导的机器学习（ML）和量子计算应用。完整的RMM编码了喷注、b喷注、轻子、光子和缺失横向能量之间的详细成对相关性，但每个事件包含超过千个值，使大规模训练计算负担沉重，并且与当前低量子位量子设备不兼容。所提出的RMM-C46 ML输入空间通过聚合不变质量、快速性差值和横向能量成分来保持RMM的物理块结构，将原始RMM的尺寸减少了一个数量级，同时保持可解释性。应用于模拟的质子-质子碰撞，中心对称能为13.6 TeV，这些表示在监督和非监督ML任务中与完整RMM的判别性能相匹配或超过。其紧凑性、稳定性以及物理透明性也使其自然适用于近期内的量子机器学习架构。RMM-C46为下一代碰撞物理分析提供了可扩展、高效且量子友好的完整RMM替代方案。
英文摘要：
We introduce a compact, physics-driven event representation, RMM-C46, designed to compress the high-dimensional rapidity mass matrix (RMM) into a low-dimensional, interpretable feature set suitable for physics-informed machine learning (ML) and quantum computing applications. The full RMM encodes detailed pairwise correlations among jets, b-jets, leptons, photons, and missing transverse energy but contains more than a thousand values per event, making it computationally heavy for large-scale training and incompatible with current low-qubit quantum devices. The proposed RMM-C46 input space for ML preserves the physical block structure of the RMM through aggregated invariant mass, rapidity difference, and transverse energy components, reducing the size of the original RMM by over an order of magnitude while maintaining interpretability. Applied to simulated proton-proton collisions at centre-of-mass energy of 13.6 TeV, these representations match or exceed the discriminative performance of the full RMM in both supervised and unsupervised ML tasks. Their compactness, stability, and physics transparency also make them naturally compatible with near-term quantum machine learning architectures. RMM-C46 provides a scalable, efficient, and quantum-ready alternative to the full RMM for next-generation collider physics analyses.
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