![[2602.20475] PhyGHT: Physics-Guided HyperGraph Transformer for Signal Purification at the HL-LHC](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.20475] PhyGHT: Physics-Guided HyperGraph Transformer for Signal Purification at the HL-LHC
Summary
The article presents PhyGHT, a Physics-Guided HyperGraph Transformer designed to enhance signal purification at the High-Luminosity Large Hadron Collider (HL-LHC) by effectively filtering noise from particle collision data.
Why It Matters
As the HL-LHC aims to uncover fundamental properties of the universe, the ability to accurately extract signals from overwhelming background noise is crucial. PhyGHT's innovative approach could significantly improve data analysis in high-energy physics, fostering advancements in scientific discovery.
Key Takeaways
- PhyGHT combines local graph attention with global self-attention for better signal extraction.
- The model introduces a Pileup Suppression Gate to filter noise effectively.
- PhyGHT outperforms existing methods in predicting energy and mass correction factors.
- A novel simulated dataset for top-quark pair production is released for validation.
- Interdisciplinary collaboration enhances the potential for scientific breakthroughs at the HL-LHC.
High Energy Physics - Experiment arXiv:2602.20475 (hep-ex) [Submitted on 24 Feb 2026] Title:PhyGHT: Physics-Guided HyperGraph Transformer for Signal Purification at the HL-LHC Authors:Mohammed Rakib, Luke Vaughan, Shivang Patel, Flera Rizatdinova, Alexander Khanov, Atriya Sen View a PDF of the paper titled PhyGHT: Physics-Guided HyperGraph Transformer for Signal Purification at the HL-LHC, by Mohammed Rakib and 5 other authors View PDF HTML (experimental) 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 con...
