arXiv Machine Learning
Likelihood-Based Unsupervised Anomaly Detection in CMS Dijet Events
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arXiv Machine Learning
Aug 17
Contrastive Learning for Interpretable Anomaly Detection at Collider Experiments
arXiv:2608. 13652v1 Announce Type: new Abstract: Generic event-level anomaly detection for collider physics has two recurring problems: anomaly scores are hard to interpret, and they correlate strongly with energy scale and object multiplicity.
arXiv Machine Learning
Jul 20
Learning Standard Model structure from LHC data with Riemannian flow matching
arXiv:2607. 16144v1 Announce Type: cross Abstract: In this work we demonstrate that a single transformer-based generative model can capture Standard Model structure spanning five decades of invariant mass, from the sub-GeV regime to the TeV continuum, a range that no single Monte Carlo sample covers.
arXiv Machine Learning
Jun 25
KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging
arXiv:2512. 07420v3 Announce Type: replace-cross Abstract: Jet identification plays a central role in analyzing data from high-energy collider experiments.
arXiv Machine Learning
Jun 15
Machine-learned particle flow as a foundation model for collider physics
arXiv:2606. 14373v1 Announce Type: cross Abstract: The workflow from particle collision to physics analysis passes through a series of reconstruction steps that are traditionally modular and disconnected, with no shared representation linking low-level detector data to high-level analysis tasks.
arXiv Machine Learning
Jun 8
ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics
arXiv:2604. 01313v2 Announce Type: replace Abstract: High-fidelity simulations and complex inverse problems, such as detector modeling and unfolding, are computationally intensive bottlenecks across subatomic physics, yet essential for accurate physical interpretation.
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.