arXiv Machine Learning
Jun 18
Predicting the Neutrino Mass Ordering Using Neural Networks
arXiv:2606. 03745v1 Announce Type: cross Abstract: Determining the neutrino mass ordering remains a central open problem in particle physics.
Uncovering Hidden Leptonic Correlations with Flow Matching and Autoencoders
arXiv:2608. 15042v1 Announce Type: cross Abstract: We perform a global search for values of the Yukawa matrices and Majorana masses in the Type-I seesaw mechanism.
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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
Sep 16
Deep-learning-based low-energy trigger algorithms for the Hyper-Kamiokande experiment
The paper presents deep‑learning trigger algorithms for the Hyper‑Kamiokande water Cherenkov detector, targeting low‑energy neutrino events below 7 MeV. It compares a supervised neural‑network classifier with two anomaly‑detection methods—an autoencoder and a Manifold Projection‑Diffusion Recovery model—showing the supervised model achieves a 76.7 % signal efficiency for 3 MeV electrons, far surpassing the 26.4 % efficiency of a traditional hit‑count trigger. GPU‑based runtime tests indicate per‑window inference latencies well below one millisecond.
arXiv Machine Learning
Aug 27
Finding and using interpretable latents in a neutrino foundation model with sparse autoencoders
The paper applies sparse autoencoders to a neutrino foundation model trained on IceCube data, uncovering a validated atlas of physical concepts within the model’s internal representation. Causal analysis shows the direction reconstruction head largely ignores this atlas, whereas an uncertainty head trained on the same representation effectively uses quality and brightness features, improving angular resolution from 20.2° to 3.2° at 20% efficiency. These findings demonstrate that mechanistic interpretability can expose latent physics and guide the design of downstream tasks.
arXiv AI
Jun 16
JetParticle-JEPA: An Efficient Self-Supervised Representation Learning method for Jet Tagging in High-Energy Physics
arXiv:2606. 14813v1 Announce Type: cross Abstract: Jet tagging at the Large Hadron Collider increasingly relies on deep learning models trained on massive simulated datasets, leading to high computational costs and limited robustness to detector mismodeling.
Hugging Face Trending Papers
Aug 24
Neural Boltzmann Equations
The dynamics of particles in the early universe are described by Boltzmann equations, which involve high-dimensional phase-space integrals. Classical approaches use quadrature integration and evolve t...
arXiv Machine Learning
Sep 17
Comprehensive reconstruction of collider events with hypergraph representation learning and graph-conditioned diffusion
VyPER is a new geometric learning framework that reconstructs particle collider events by representing them as hypergraphs with a physics-inspired topology. It tackles two key tasks: assigning measured jets and leptons to their parent particles through supervised hyperedge classification, and predicting neutrino kinematics using a diffusion model, all optimized jointly with a shared loss function. The authors evaluate VyPER on various proton‑proton collision processes, showing improved performance over existing analytical and machine‑learning methods and enabling more precise measurements in Higgs, electroweak, and top‑quark studies.
arXiv Machine Learning
Sep 10
Likelihood-Based Unsupervised Anomaly Detection in CMS Dijet Events
arXiv:2609.06686v1 Announce Type: cross Abstract: We present an unsupervised search for anomalous dijet events in proton--proton collision data using neural spline flow density estimation. A normaliz...
arXiv Statistics ML
Aug 25
Neural Boltzmann Equations
arXiv:2608.23022v1 Announce Type: cross Abstract: The dynamics of particles in the early universe are described by Boltzmann equations, which involve high-dimensional phase-space integrals. Classical...
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
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.
arXiv AI
Jun 19
Towards Engineering Scaling Laws with Pretraining Data Composition
arXiv:2606. 19781v1 Announce Type: cross Abstract: Neural scaling laws describe how model performance improves as a power law in compute, model size, and dataset size.