arXiv Machine Learning
AllShowers: One model for all calorimeter showers
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arXiv Machine Learning
Jun 4
CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters
arXiv:2606. 04165v1 Announce Type: cross Abstract: High-precision calorimeter simulation at current and future colliders imposes rapidly growing computational demands, motivating the development of machine-learning surrogates for traditional Monte Carlo tools such as Geant4.
arXiv Machine Learning
Jun 11
SPADE: Split-and-Delay Embeddings for Autoregressive High-Granularity Calorimeter Simulation
arXiv:2606. 11304v1 Announce Type: cross Abstract: We introduce SPADE (SPlit And Delay Embeddings), an autoregressive transformer for sequences whose tokens carry multiple features.
arXiv Machine Learning
Jun 16
GPT-Based Fast Simulation of CLAS12 Detector Hits via Conditional Autoregressive Generation
arXiv:2606. 16035v1 Announce Type: cross Abstract: Modern particles physics experiments have demonstrated an increasing need for fast, high-fidelity detector simulation as detector components have improved and subsequent computational requirements approach the limits of available resources.
arXiv Machine Learning
Aug 14
Fine-tuned Normalizing Flows for ALICE Zero Degree Calorimeter Fast Simulation
arXiv:2608. 12795v1 Announce Type: cross Abstract: Simulating the ALICE Zero Degree Calorimeter (ZDC) neutron detector responses at the LHC is computationally expensive, requiring complex Monte Carlo chains.
Hugging Face Trending Papers
Aug 13
Fine-tuned Normalizing Flows for ALICE Zero Degree Calorimeter Fast Simulation
Simulating the ALICE Zero Degree Calorimeter (ZDC) neutron detector responses at the LHC is computationally expensive, requiring complex Monte Carlo chains. We develop a generative surrogate, focusing on Normalizing Flows (NFs).
arXiv Machine Learning
Sep 10
Mind the Gap: Navigating Inference with Optimal Transport Maps
The paper introduces a model calibration method using optimal transport to address discrepancies between simulation and experimental data in high-dimensional machine learning applications. Applied to jet tagging in particle physics, the technique calibrates a 128‑dimensional latent representation from a general‑purpose classifier, ensuring downstream derived quantities are properly calibrated. This enables more reliable use of foundation models for jet flavor analysis in LHC experiments and offers a general framework for correcting high‑dimensional simulations across scientific fields.