PhyGHT: Physics-Guided HyperGraph Transformer for Signal Purification at the HL-LHC

2026-02-24

AI Summary

This work addresses the challenge posed by intense background noise from approximately 200 simultaneous pileup collisions at the High-Luminosity Large Hadron Collider (HL-LHC), which severely degrades the precision of key physics observables. To mitigate this, we propose PhyGHT, a novel model that integrates a physically constrained, interpretable Pileup Suppression Gate (PSG) with a Hypergraph Transformer. By combining distance-aware local graph attention and global self-attention mechanisms, PhyGHT explicitly models the physical topology of particle showers and filters out soft pileup noise prior to hypergraph aggregation. Evaluated on a newly released extreme pileup simulation dataset for top-quark pair production, PhyGHT outperforms current state-of-the-art methods from ATLAS and CMS in predicting energy and mass correction factors, and achieves high-fidelity reconstruction of the top-quark invariant mass.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Humans and AI: Human-in-the-loop Machine Learning
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

The High-Luminosity Large Hadron Collider (HL-LHC) at CERN will produce unprecedented datasets capable of revealing fundamental properties of the universe. However, realizing its discovery potential faces a significant challenge: extracting small signal fractions from overwhelming backgrounds dominated by approximately 200 simultaneous pileup collisions. This extreme noise severely distorts the physical observables required for accurate reconstruction. To address this, we introduce the Physics-Guided Hypergraph Transformer (PhyGHT), a hybrid architecture that combines distance-aware local graph attention with global self-attention to mirror the physical topology of particle showers formed in proton-proton collisions. Crucially, we integrate a Pileup Suppression Gate (PSG), an interpretable, physics-constrained mechanism that explicitly learns to filter soft noise prior to hypergraph aggregation. To validate our approach, we release a novel simulated dataset of top-quark pair production to model extreme pileup conditions. PhyGHT outperforms state-of-the-art baselines from the ATLAS and CMS experiments in predicting the signal's energy and mass correction factors. By accurately reconstructing the top quark's invariant mass, we demonstrate how machine learning innovation and interdisciplinary collaboration can directly advance scientific discovery at the frontiers of experimental physics and enhance the HL-LHC's discovery potential. The dataset and code are available at https://github.com/rAIson-Lab/PhyGHT

Problem

• pileup
• signal purification
• HL-LHC
• background suppression
• particle reconstruction

Innovation

• Physics-Guided HyperGraph Transformer
• Pileup Suppression Gate
• Signal Purification
• Top-Quark Reconstruction
• High-Luminosity LHC