IRC-safe optimal-transport metrics (EMD, sEMD) enable lower-bias cell resampling of negative-weight NLO Monte Carlo events without intermediate jet clustering.
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Cell-level Transformers classify collimated ALP photon-jets versus single photons with AUC 0.98 and regress diphoton mass to ~64 MeV, beating shower-shape and other ML baselines in an ATLAS-like GEANT4 simulation.
A review summarizing advancements in probing quantum entanglement and Bell inequalities using high-energy particle colliders.
citing papers explorer
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Optimal-Transport-Based Cell Resampling for Negative and Pathological Event Weights
IRC-safe optimal-transport metrics (EMD, sEMD) enable lower-bias cell resampling of negative-weight NLO Monte Carlo events without intermediate jet clustering.
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Transformer-based machine learning using low-level calorimeter signals for collimated photon identification at collider experiments
Cell-level Transformers classify collimated ALP photon-jets versus single photons with AUC 0.98 and regress diphoton mass to ~64 MeV, beating shower-shape and other ML baselines in an ATLAS-like GEANT4 simulation.
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Particle Collisions & Quantum Entanglement in High-Energy Collisions
A review summarizing advancements in probing quantum entanglement and Bell inequalities using high-energy particle colliders.