High-Energy Physics

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

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Learning Standard Model structure from LHC data with Riemannian flow matching
Jul 17, 2026Midori Kato, Kevin A. Urquía-Calderón, Inar Timiryasov +1Constrained Generative Modeling Flow Matching
Niels Bohr Institute, University of Copenhagen, Jagtvej 155A, DK-2200, Copenhagen, Denmark
LQCDMaster: Agentic Scientific Computing for Lattice Quantum Chromodynamics Research
Jul 16, 2026Haofei Gao, Tingjia Miao, Wenkai Jin +12Scientific Code Generation High-Energy Physics
School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, 200240, China. · Tsung-Dao Lee Institute, Shanghai Jiao Tong University, Shanghai, 200240, China. · State Key Laboratory of Dark Matter Physics, Shanghai Jiao Tong University, Shanghai, 200240, China. +6
Mono-Z Dark Matter Search with Neural Spline Flows Using CMS Run 2015D Open Data
Jul 15, 2026Hitesh Rasineni, Bhavishya ChebroluHigh-Energy Physics
VIT-AP University, Amaravati, India · 1VIT-AP University, Amaravati, 522241, India · Mohan Babu University, Tirupati, India +1
Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology
Jul 14, 2026Jorge Alda, Jacobo Asorey, Alejandro Mir +1Parameter Estimation High-Energy Physics
Dipartimento di Fisica e Astronomia “Galileo Galilei”, Universit`a degli Studi di Padova & INFN Sezione di Padova, via Marzolo 8, 35131 Padova, Italy · Centro de Astropart´ıculas y F´ısica de Altas Energ´ıas (CAPA), Pedro Cerbuna 12 50009 Zaragoza, Spain · Departamento de F´ısica Te´orica, Facultad de Ciencias, Universidad de Zaragoza, Pedro Cerbuna 12, E-50009 Zaragoza, Spain
Revisiting One-Zero and Two-Zero Neutrino Mass Textures in Light of Recent Oscillation and Cosmological Data
Jul 9, 2026Haruto Kitagawa, Coh Miyao, Satsuki Nishimura +1High-Energy Physics
Department of Physics, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan · Department of Physics and Astronomy, Faculty of Science and Technology, Tokyo University of Science, Yamazaki, Noda, Chiba 278-8510, Japan · Department of Physics, Kyoto University, Kitashirakawa-Oiwakecho, Sakyo-ku, Kyoto 606-8502, Japan +1
Graph Neural Networks for the Graphical Bootstrap
Jul 3, 2026Rigers Aliaj, Gabriele Dian, Reza Doobary +1Graph Neural Networks High-Energy Physics
Deutsches Elektronen-Synchrotron DESY Hamburg, Germany · Independent Researcher United Kingdom · Department of Mathematical Sciences, Durham University Durham, United Kingdom
Gravitational Duals from Equations of State II: Large Hierarchies and False Vacua
Jun 29, 2026Raul Jimenez, David Mateos, Pavlos Protopapas +3High-Energy Physics Physics-Informed ML
Institut de Ciències del Cosmos (ICC), Universitat de Barcelona, Martí i Franquès 1, ES-08028, Barcelona, Spain. · Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, ES-08010, Barcelona, Spain. · Institute for Applied Computational Science, Harvard University, Cambridge, MA.
Sampling the Schwinger Model with Gauge-Equivariant Diffusion
Jun 25, 2026Octavio Vega, Aida X. El-KhadraDiffusion Sampling Generative Modeling
Department of Physics University of Illinois Urbana-Champaign Urbana, IL 61801
Learning to Trigger: Reinforcement Learning at the Large Hadron Collider
Jun 22, 2026Zixin Ding, Shaghayegh Emami, Giovanna Salvi +7RL Control Non-Stationary RL
University of Chicago · University of Michigan, Ann Arbor · Fermi National Accelerator Laboratory
Ultra-Peripheral Collisions as a Nuclear-Structure Interferometer with Interpretable Multitask Deep Learning
Jun 22, 2026Jing-Zong Zhang, Wang-Mei Zha, Lingxiao Wang +1Interpretable ML High-Energy Physics
Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Institute of Modern Physics, Fudan University, Shanghai 200433, China · Department of Modern Physics, University of Science and Technology of China, Hefei 230026, China · RIKEN Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS), Wako, Saitama 351-0198, Japan +2
What Do Lorentz-Equivariant Jet Taggers Learn?
Jun 19, 2026Jay Agarwal, Siddharth Khare, Dhruv KumarJet Tagging Equivariant Neural Networks
BITS Pilani, Pilani, India.
HEPTv2: End-to-End Efficient Point Transformer for Charged Particle Reconstruction
Jun 18, 2026Siqi Miao, Shitij Govil, Jack P. Rodgers +5Efficient Transformer Inference Transformer
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA. · Department of Physics and Astronomy, Purdue University, West Lafayette, IN, USA. · Department of Physics, University of California San Diego, La Jolla, CA, USA. +1
Towards Engineering Scaling Laws with Pretraining Data Composition
Jun 18, 2026Jan-Lucas Uslu, Kevin Greif, Daniel Whiteson +1Synthetic Data Pretraining High-Energy Physics
Department of Applied Physics Stanford University Stanford, CA 94305, USA · Department of Physics and Astronomy University of California, Irvine Irvine, CA 92697, USA · Department of Particle Physics and Astrophysics SLAC National Accelerator Laboratory Stanford University Menlo Park, CA 94025, USA
Correcting Sensor-Induced Distribution Drift with Wasserstein Adversarial Learning
Jun 17, 2026Saraa Ali, Vladimir Bocharnikov, Fedor Ratnikov +3High-Energy Physics Generative Adversarial Networks
Laboratory of Methods for Big Data Analysis, HSE University, Pokrovsky Boulevard 11, Moscow 109028, Russia
Reconfigurable Computing Challenge: Transformer for Jet Tagging on Versal AI Engines
Jun 16, 2026Gram Koski, Sean Lipps, Zhenghua Ma +2Efficient Transformer Inference Efficient Neural Network Inference
Department of Computer Science and Engineering University of California San Diego La Jolla, CA, USA
GPT-Based Fast Simulation of CLAS12 Detector Hits via Conditional Autoregressive Generation
Jun 14, 2026Cole Granger, James Giroux, Richard Tyson +2Neural Surrogate Modeling High-Energy Physics
William & Mary, Department of Data Science, Williamsburg, VA 23185, USA · University of Glasgow, School of Physics and Astronomy, Glasgow G12 8QQ,14 UK · Thomas Jefferson National Accelerator Facility, Newport News, VA 23606, USA +1
Learning the generating functional for variance reduction in lattice QCD
Jun 14, 2026Ryan Abbott, Yang Fu, Daniel C. Hackett +3High-Energy Physics Variance Reduction
1Physics Department, Columbia University, New York, NY 10027, USA · Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · 3The NSF AI Institute for Artificial Intelligence and Fundamental Interactions +3
Pre-Training for Simulation-Based Science: A Study on Jet Foundation Model Training Objectives
Jun 12, 2026Ibrahim Elsharkawy, Joschka Birk, Vinicius Mikuni +3Self-Supervised Pre-Training High-Energy Physics
Department of Physics, University of Toronto and Vector Institute, Toronto, ON, Canada · NERSC, Lawrence Berkeley National Laboratory, Berkeley, California, USA · Institut für Experimentalphysik, Universität Hamburg, 22761 Hamburg, Germany +3
Machine-learned particle flow as a foundation model for collider physics
Jun 12, 2026Farouk Mokhtar, Joosep Pata, Michael Kagan +1Representation Learning High-Energy Physics
University of California San Diego, La Jolla, California 92093, USA · National Institute of Chemical Physics and Biophysics, Tallinn 12618, Estonia · SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
JetParticle-JEPA: An Efficient Self-Supervised Representation Learning method for Jet Tagging in High-Energy Physics
Jun 12, 2026Guillaume Letellier, Antonin Vacheret, Frédéric JurieSelf-Supervised Learning Joint-Embedding Predictive Architecture
LPCC · GREYC, Normandy University, Unicaen, ENSICAEN, UMR CNRS 6072, F-14000 Caen, France · LPC, Normandy University, Unicaen, ENSICAEN, IN2P3, UMR CNRS 6534, F-14000 Caen, France
AgentRivet: an automated system for producing Rivet routines from journal publications
Jun 11, 2026Antonio J. Costa, Caterina Doglioni, Christian Gütschow +2Software Engineering Agents Code Generation
Department of Physics & Astronomy, University of Manchester, Manchester M13 9PL, United Kingdom · Centre for Advanced Research Computing, University College London, London WC1E 6BT, United Kingdom
Efficient AI-Inspired Reduction of Feynman Integrals via Tube Seeding
Jun 9, 2026Justin Berman, Francois Charton, Andres Luna +2High-Energy Physics
Leinweber Institute for Theoretical Physics, Randall Laboratory of Physics, University of Michigan, Ann Arbor, 450 Church St, Ann Arbor, MI 48109-1040, USA · Axiom Math, 124 University Avenue, Palo Alto, California, 94301, United States · CERMICS, Ecole Nationale des Ponts et Chauss´ees, 8 Avenue Blaise Pascal, 77420, Champs sur Marne +3
Agentic Hybrid RAG for Evidence-Grounded Muon Collider Analysis
Jun 9, 2026Ruobing Jiang, Dawei Fu, Cheng Jiang +6Scientific QA High-Energy Physics
State Key Laboratory of Nuclear Physics and Technology, Peking University, China
Predicting the Neutrino Mass Ordering Using Neural Networks
Jun 2, 2026T. J. C. Bezerra, L. Asquith, E. Bannister +1High-Energy Physics Feedforward Neural Networks
Department of Physics and Astronomy, University of Sussex, Brighton, BN1 9QH, United Kingdom · †Now at Universal Quantum Ltd., Haywards Heath, RH16 1XQ, United Kingdom
Neutrino Fingerprints: Image-Based Encodings of IceCube Events for CNN Direction Reconstruction
Jun 1, 2026Floriano Tori, Brecht Verbeken, Vincent GinisDirection-of-Arrival Estimation High-Energy Physics
Data Analytics Lab, Vrije Universiteit Brussel, Pleinlaan 5, 1050 Brussels, Belgium · imec-SMIT, Vrije Universiteit Brussel, Pleinlaan 9, 1050 Brussels, Belgium · School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA
Deep-learning-based low-energy trigger algorithms for the Hyper-Kamiokande experiment
May 29, 2026Katharina Lachner, Saúl Alonso-Monsalve, Benjamin Richards +1High-Energy Physics Reconstruction-Based Anomaly Detection
ETH Zürich · University of Warwick
A new completely parameter-free clustering algorithm for unsupervised classification of BATSE gamma-ray bursts
May 28, 2026Soumita ModakClustering Unsupervised Clustering
Department of Statistics, Presidency University 86/1 College Street, Kolkata-700073, India
Neural Scaling Laws for Jet Generation
May 27, 2026Oz Amram, Darius A. Faroughy, Tjarko Gerdes +5High-Energy Physics Autoregressive Generation
Fermi National Accelerator Laboratory, Batavia, IL 60510, USA · NHETC, Dept. of Physics and Astronomy, Rutgers University, Piscataway, NJ 08854, USA · Institute for Experimental Physics, Universität Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany +1
Particle-Lund Multimodality in Jet Taggers
May 26, 2026Loukas Gouskos, Benedikt MaierUnified Multimodal Models Jet Tagging
Brown University, Providence, USA · Imperial College of Science, Technology and Medicine, London, United Kingdom
Considerations for an Integrated Detector Design at FCC-ee: A Human-AI Exploration
May 25, 2026Charles YoungAI-Assisted Scientific Research High-Energy Physics
SLAC National Accelerator Laboratory

Jul 17, 2026J/K move · Enter open · S save

Learning Standard Model structure from LHC data with Riemannian flow matching

Midori Kato, Kevin A. Urquía-Calderón, Inar Timiryasov, Oleg Ruchayskiy

Niels Bohr Institute, University of Copenhagen, Jagtvej 155A, DK-2200, Copenhagen, Denmark

Constrained Generative Modeling Flow Matching Conditional Flow Matching Riemannian Flow Matching High-Energy Physics

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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. To achieve this we design ShellFlow, a Riemannian conditional flow matching model that, given the recorded event composition, generates each particle on its on-shell manifold. Its only physics priors are the on-shell condition and the invariant-mass formula. The model is trained on

\sim 10^{9}

real

pp

collision events from the ATLAS Open Data 13~TeV release and told nothing else. From a single training run, the model learns to reproduce all of the following: intra-particle kinematics, the dilepton resonances (

J/ψ

Υ

Z

) at their PDG positions, the leptonic Weinberg angle, the

W

and top-quark masses, and inter-particle correlations that enter no training objective. A substantial fraction of the Standard Model is thus learnable directly from recorded collision data.