AI RESEARCH
Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems
arXiv CS.LG
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ArXi:2507.19205v2 Announce Type: replace Real-time particle transverse momentum ($p_T$) estimation in high-energy physics demands algorithms that are both efficient and accurate under strict hardware constraints. Static machine learning models degrade under high pileup and lack physics-aware optimization, while generic graph neural networks (GNNs) often neglect domain structure critical for robust $p_T$ regression.