FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
Ultra-high-granularity detector simulation with intra-event aware generative adversarial network and self-supervised relational reasoning
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BRICKS creates compositional neural Markov kernels via hybrid transformers and Riemannian Flow Matching on product manifolds to enable zero-shot simulation of radiation-matter interactions across arbitrary material distributions.
A GNN pretrained on 120M simulated HEP events generalizes to unseen processes and ATLAS data; fine-tuning boosts accuracy especially with small datasets, with CKA showing preserved encoders but altered intermediate layers.
citing papers explorer
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Local Conformal Predictions for Calibrated Surrogates
FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
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BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulation
BRICKS creates compositional neural Markov kernels via hybrid transformers and Riemannian Flow Matching on product manifolds to enable zero-shot simulation of radiation-matter interactions across arbitrary material distributions.
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Pretrained Event Classification Model for High Energy Physics Analysis
A GNN pretrained on 120M simulated HEP events generalizes to unseen processes and ATLAS data; fine-tuning boosts accuracy especially with small datasets, with CKA showing preserved encoders but altered intermediate layers.