Learning from all particles in high-energy collisions

TL;DR

This paper introduces AI-driven methods that utilize all particles in collider events to significantly improve the precision of Higgs measurements and enhance the discovery potential of high-energy physics experiments.

Contribution

The authors propose the holistic approach and Advanced Color Singlet Identification, leveraging deep learning to infer particle parentage and improve signal-background separation.

Findings

Up to sixfold improvement in Higgs measurement precision

Realistic prospects for observing rare Higgs decays

Enhanced signal-background separation using all reconstructed particles

Abstract

Particle colliders stand as an irreplaceable pillar of inquiry for exploring the fundamental building blocks of matter and forces of the Universe, yet fully decoding complex collision event information remains a significant challenge. Recent advances in artificial intelligence (AI) have revolutionized complex data analysis across scientific disciplines, inspiring novel strategies to extract the rich information embedded in collider events. Here we introduce two complementary concepts -- the holistic approach and Advanced Color Singlet Identification -- to enhance signal-background separation, which is a critical prerequisite for precise physics measurements. By leveraging all reconstructed particles and inferring their parentage via deep learning, these methods improve the precision of key Higgs physics benchmark measurements by up to sixfold and enable realistic prospects for observing rare Higgs decays previously deemed inaccessible. Our results demonstrate how integrating particle-level information with modern AI technologies can substantially boost the discovery potential of high-energy colliders, paving a new path to unravel the fundamental physical laws underlying particle physics experiments.