Compact Representation of Particle-Collision Events for Physics-Informed Machine Learning

TL;DR

This paper presents RMM-C46, a compact, physics-informed feature set derived from the high-dimensional RMM, enabling efficient machine learning and quantum computing applications in collider physics while preserving interpretability and discriminative power.

Contribution

The authors introduce RMM-C46, a reduced, interpretable representation of the RMM that maintains physics information and improves computational efficiency for ML and quantum applications.

Findings

RMM-C46 reduces RMM size by over an order of magnitude.

RMM-C46 matches or exceeds full RMM performance in ML tasks.

The representation is compatible with near-term quantum machine learning architectures.

Abstract

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.