arXiv:2602.20475v2 Announce Type: replace-cross 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 h
The High-Luminosity Large Hadron Collider (HL-LHC) is approaching, requiring advanced methods to handle its unprecedented data volume and complexity.
Developing new AI techniques for extreme data environments like particle physics pushes the boundaries of signal processing and machine learning, with potential applications in other complex data challenges.
The ability to extract critical scientific signals from massive, noisy datasets at the HL-LHC is significantly improved, enabling new discoveries.
- · CERN
- · High-energy physics researchers
- · AI/ML for scientific discovery
- · Hypergraph Transformer developers
- · Traditional signal processing methods in high-noise environments
Experimental particle physics will achieve higher precision and potentially new discoveries regarding fundamental properties of the universe.
The advances in Physics-Guided Hypergraph Transformers could be adapted for other data-intensive scientific fields or industrial applications where noise is a major problem.
These highly specialized AI models might require significant computational resources, further driving demand for advanced hardware and efficient AI architectures.
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Read at arXiv cs.LG