Physics-informed GNNs with four detector-aware graph constructions and a custom message passing layer achieve MAE 0.8525 for pT estimation on CMS trigger data with over 55% fewer parameters than baselines.
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Environmental radiation measurements combined with Geant4 simulations predict a residual background of approximately 250 events per day per kg per keV in the 10-100 eV range for NUCLEUS CaWO4 detectors, dominated by cosmic-ray neutrons after over 100x rejection.
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Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems
Physics-informed GNNs with four detector-aware graph constructions and a custom message passing layer achieve MAE 0.8525 for pT estimation on CMS trigger data with over 55% fewer parameters than baselines.
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Particle background characterization and prediction for the NUCLEUS reactor CE$\nu$NS experiment
Environmental radiation measurements combined with Geant4 simulations predict a residual background of approximately 250 events per day per kg per keV in the 10-100 eV range for NUCLEUS CaWO4 detectors, dominated by cosmic-ray neutrons after over 100x rejection.