High-Energy Physics

Latest papers 151

25 papers in the last four weeks, up 39% on the four weeks before. 0.1% of all new papers.

Data Free Training of Diffusion Models for Lattice Field Theories
Oct 13, 2026Dennis Grinwald, Ankur Singha, Khaled Kahouli +2Diffusion Model Sampling Diffusion Models
Berlin Institute for the Foundations of Learning and Data (BIFOLD), Germany · Technische Universität Berlin, Germany · Deutsches Elektronen-Synchrotron (DESY), Platanenallee 6, 15738 Zeuthen, Germany +2
Emergent Loop Geometry in Gauge-Equivariant Graph Neural Networks
Oct 13, 2026Ali Rayat, Gia-Wei ChernEquivariant GNNs GNN Expressivity
Department of Physics, University of Virginia, Charlottesville, VA 22904, USA
Deep learning methods for gamma event selection in TAIGA-IACT image analysis in stereo-mode
Oct 12, 2026E. O. Gres, A. P. Kryukov, P. A. Volchigov +6Classification High-Energy Physics
Irkutsk State University, Institute of Applied Physics, Irkutsk, 664003, blv. Gagarina, 20, Russia · Lomonosov Moscow State University, Skobeltsyn Institute of Nuclear Physics, Moscow, 119991 Leninskie gory,1, bld.2, Russia
Using Line Segment Tracking to enable ML-based Track Reconstruction in CMS
Oct 11, 2026Aashay Arora on behalf of the CMS CollaborationHigh-Energy Physics
University of California San Diego, La Jolla, CA, USA
RINO: Renormalization Group Distillation with No Labels
Oct 11, 2026Zichun Hao, Raghav Kansal, Abhijith Gandrakota +6Self-Supervised Learning High-Energy Physics
Division of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125 · Physics Division, Fermi National Accelerator Laboratory, Batavia, IL 60510
Local Diagnostics of Learned Event Representations for Anomaly Detection at the HL-LHC
Oct 9, 2026Donatella Genovese, Philip Ploner, Simone Scardapane +2Anomaly Detection Interpretable Anomaly Detection
Department of Computer, Control and Management Engineering, Sapienza University of Rome · Department of Physics, ETH Zurich · Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome +1
Machine Learning Meets High-Energy Nuclear Physics: From Pattern Recognition to Physics-Integrated Discovery
Oct 8, 2026Xun Chen, Weiyao Ke, Yu-Gang Ma +2High-Energy Physics Physics-Informed ML
School of Nuclear Science and Technology, University of South China, Hengyang 421001, China · INFN — Istituto Nazionale di Fisica Nucleare — Sezione di Bari, Via Orabona 4, 70125 Bari, Italy · Key Laboratory of Quark and Lepton Physics (MOE) & Institute of Particle Physics, Central China Normal University, Wuhan, Hubei 430079, China +6
Sampling SU(N) gauge theory on a 2D lattice from independent plaquettes via holonomies and corner reweighting
Oct 6, 2026Javad KomijaniHigh-Energy Physics Importance Sampling
A method for multimodal analysis of TAIGA experiment data using essential features
Oct 6, 2026Alexander Kryukov, Julia Dubenskaya, Elena Fedotova +6Multimodal Sensor Fusion High-Energy Physics
Lomonosov Moscow State University, Skobeltsyn Institute of Nuclear Physics, Moscow, 119991 Russia · Research Institute of Applied Physics, Irkutsk State University, Irkutsk, 664003 Russia
How to scale your HEP ML models: A recipe for robust architecture comparisons at scale
Oct 5, 2026Matthias Vigl, Nikita Pond, Jackson Barr +5High-Energy Physics Scaling Laws
Technical University of Munich · SLAC National Accelerator Laboratory · University College London +4
Application of sequence learning for predicting radiation damage of the CMS electromagnetic calorimeter
Oct 2, 2026Mario Ivan Gallegos Torres, Leonid Serkin, Guy PaicHigh-Energy Physics Time Series Forecasting
Facultad de Ciencias, Universidad Nacional Autónoma de México, Circuito Exterior s/n, Ciudad de México 04510, México. · Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Apartado Postal 70-543, Ciudad de México 04510, México.
Conditional Capacity and Routing in Mixture-of-Experts Particle Transformers
Oct 2, 2026Kaushik Pendiyala, Haris Zia, Trevin Lee +8Mixture-of-Experts Models Sparse Mixture-of-Experts
University of California Davis, Davis, CA, USA · University of California San Diego, San Diego, CA, USA · University of Illinois at Chicago, Chicago, IL, USA +1
PINNing the pion: conformal deep learning for $F_π(s)$ and the $(g-2)_μ$ hadronic contribution
Sep 30, 2026Mayank Goel, Subhadip Mitra, Monalisa PatraHigh-Energy Physics Physics-Informed ML
Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
Searching for BSM Experimental Signatures with Large Lagrangian Models
Sep 29, 2026Ibrahim Elsharkawy, Victoria Knapp-Perez, Wahid Bhimji +1AI for Science High-Energy Physics
Department of Physics, University of Toronto and Vector Institute, Toronto, ON, Canada · NERSC, Lawrence Berkeley National Laboratory, Berkeley, California, USA · Department of Physics and Astronomy, University of California, Irvine, CA 92697 +3
Hybrid Neural Simulation-Based Inference for Robust Applications and Limited-Budget Scenarios
Sep 28, 2026Sean Benevedes, Mani Dehghan, Aishik Ghosh +1Simulation-Based Inference Cost-Aware Inference
School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA · School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA and Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA · Max Planck Institute for Physics, Boltzmannstr. 8, 85748 Garching, Germany
Infrared Subtraction with Artificial Intelligence
Sep 28, 2026Wenjie He, Xiaohui Liu, Yandong Liu +1High-Energy Physics
School of Physics and Astronomy, Beijing Normal University, and Key Laboratory of Multiscale Spin Physics (Beijing Normal University), Ministry of Education, Beijing 100875, China · Southern Center for Nuclear Science Theory (SCNT), Institute of Modern Physics, Chinese Academy of Science, Huizhou 516000, China
Solver Agent: an Agentic AI Framework for Theoretical Physics Computations Applied to F-theory Uplifts of O3-planes and S-folds
Sep 28, 2026Eliott Morgensztern, Cesar Fierro Cota, Alessandro MininnoAI Agents for Scientific Discovery LLM Agent Verification
Sorbonne Université, CNRS, Laboratoire de Physique Théorique et Hautes Energies, Campus Pierre et Marie Curie, 4 place Jussieu, F-75005, Paris, France · Department of Physics, University of Wisconsin–Madison, 1150 University Avenue, Madison, WI 53706, USA
Fast and Precise Learned Charged-Particle Trajectory Regression at the Large Hadron Collider
Sep 26, 2026Jonathan Renusch, Benjamin Huth, Daniel Murnane +9High-Energy Physics Efficient Neural Network Inference
CERN, Geneva, Switzerland · Technical University of Munich, Germany · Niels Bohr Institute, University of Copenhagen, Denmark +5
LaMET-Agent: An Agent Framework for Large-Momentum Effective Theory Analysis
Sep 26, 2026Jinchen He, Xiangyu Jiang, Fei Yao +1High-Energy Physics Data Analysis Agents
Fermi National Accelerator Laboratory, Batavia, IL 60510, USA · Maryland Center for Fundamental Physics, University of Maryland, College Park, MD 20742, USA · Physics Division, Argonne National Laboratory, Lemont, IL 60439, USA +3
Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Tasks
Sep 25, 2026Nilotpal Kakati, Daniel Murnane, Baran Hashemi +5Unified Multimodal Models High-Energy Physics
Weizmann Institute of Science, Rehovot, Israel · Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark · Lawrence Berkeley National Laboratory, Berkeley, CA, USA +3
Accurate Sampling from Diffusion Models
Sep 24, 2026Dénes SextyDiffusion Model Sampling High-Energy Physics
Institute of Physics, NAWI Graz, University of Graz, Universitätsplatz 5, 8010 Graz, Austria
Leveraging Industrial Foundation Models at the Edge of Particle Physics Detectors via Distillation Learning and Hardware Co-design
Sep 20, 2026Gia Ancone, Qibin Liu, Liangyu Wu +1High-Energy Physics Time-Series Foundation Models
Department of Physics Stanford University 450 Jane Stanford Way, Stanford, CA 94305, United States of America · SLAC National Accelerator Laboratory 2575 Sand Hill Road, Menlo Park, CA 94025, United States of America
The shape of quark flavors
Sep 19, 2026Shinsuke Kawai, Nobuchika OkadaHigh-Energy Physics
Faculty of Science, Yamagata University, 1-4-12 Kojirakawa-machi, Yamagata, 990-8560 Japan · Department of Physics and Astronomy, University of Alabama, Tuscaloosa, Alabama, AL35487 USA
Machine Learning for Invisible Dark Boson Searches at the Electron-Ion Collider
Sep 18, 2026Rojae Mighty, Ankush Reddy KanugantiHigh-Energy Physics Classification
Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY 11794-3800, USA · Physics Department, Brookhaven National Laboratory, Upton, NY 11973-5000, USA
Comprehensive reconstruction of collider events with hypergraph representation learning and graph-conditioned diffusion
Sep 16, 2026Lining Mao, Yvonne Peters, Ethan Simpson +1High-Energy Physics Hypergraph Learning
School of Physics and Astronomy, Shanghai Jiao Tong University, No.800 Dong Chuan Road, Shanghai 200240, PRC · Department of Physics and Astronomy, University of Manchester, Oxford Road, Manchester M13 9PL, UK
Deep learning emergent spacetime from fermionic spectral functions in holography
Sep 16, 2026Koji Hashimoto, Hyun-Sik Jeong, Keun-Young Kim +2High-Energy Physics Physics-Informed ML
Department of Physics, Kyoto University, Kyoto 606-8502, Japan · Asia Pacific Center for Theoretical Physics, Pohang 37673, Korea · Department of Physics, Pohang University of Science and Technology, Pohang 37673, Korea +3
Similarity Pairing with Energy Mover's Distance for Self-Supervised Pre-Training at the LHC
Sep 15, 2026Ho Fung Tsoi, Dylan RankinRepresentation Learning Self-Supervised Pre-Training
Department of Physics and Astronomy University of Pennsylvania, USA
Self-Supervised Learning for Robust Resonance Mass Regression in Cascade Decays
Sep 15, 2026Ho Fung Tsoi, Alex Yang, Luis Felipe Gutierrez Zagazeta +2Neural Network Robustness High-Energy Physics
Department of Physics and Astronomy University of Pennsylvania, USA · Department of Physics Kyoto University, Japan
ResoSeg: Resonance Tagger using Transformer and Segment Model
Sep 11, 2026Chunkai Li, Junhao Yin, Ke Li +1High-Energy Physics
Nankai University, Tianjin, 300071, China · University of Chinese Academy of Sciences, Beijing, 100049, China
Fast BIB simulation at a future Muon Collider with generative machine learning
Sep 10, 2026Radha Mastandrea, Shiyu Peng, Benjamin Rosser +1Tabular Diffusion Models Generative Modeling
Enrico Fermi Institute, The University of Chicago, Chicago, IL 60637, USA · Data Science Institute, The University of Chicago, Chicago, IL 60637, USA · Department of Physics, Brown University, Providence, RI 02912, USA +1

Oct 13, 2026J/K move · Enter open · S save

Data Free Training of Diffusion Models for Lattice Field Theories

Dennis Grinwald, Ankur Singha, Khaled Kahouli, Karl Jansen+1

Berlin Institute for the Foundations of Learning and Data (BIFOLD), Germany · Technische Universität Berlin, Germany · Deutsches Elektronen-Synchrotron (DESY), Platanenallee 6, 15738 Zeuthen, Germany+2

Diffusion Model Sampling Diffusion Models Score Matching High-Energy Physics

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Sampling equilibrium field configurations remains a major computational bottleneck in lattice field theory, motivating the development of generative samplers that can produce independent configurations. Diffusion models are promising for this purpose, but their standard denoising score matching objective requires samples from the target distribution, seemingly reintroducing the original sampling problem. Here, we show that diffusion models can instead be trained from their own generated samples using the target score identity, provided access to the unnormalized probability density. Importance-weighted formulations of this identity yield a consistent target throughout training, while unweighted variants define a self-consistent fixed-point training scheme. We benchmark the different approaches in two-dimensional

φ^4

theory and identify effective choices that enable efficient training and sampling across the symmetric, critical, and broken phases. Ultimately, our data-free training approach achieves comparable performance to training on HMC reference data.