High-Energy Physics

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High-energy physics investigates the basic constituents of matter and their interactions, from particle collisions and neutrino measurements to quantum field theory and the early universe. It matters because precise measurements and searches for deviations from established predictions can test the Standard Model and guide new theories. Researchers combine detector reconstruction and simulation with statistical inference, lattice calculations, and machine-learning methods such as graph neural networks for jets, transformers for tracking, and generative models for events and field configurations. A central challenge is making these methods reliable beyond simulated data, with calibrated uncertainties, interpretable results, and performance that fits the computing and latency limits of experiments.

Latest papers

164 papers22 papers in the last four weeks, down 8% on the four weeks before. 0.1% of all new papers.

Reinforcement Learning in High-Energy Physics: A Survey from Accelerator Control to Theory Exploration
Nov 1, 2026Luca AnzaloneReinforcement Learning RL Control
Independent researcher
Localized anomaly detection with a calorimeter normalizing flow
Oct 30, 2026Raunav Mendiratta, Jason Weitz, Javier DuarteAnomaly Detection Anomaly Localization
Westview High School, San Diego, USA · Department of Physics, University of California San Diego, La Jolla, USA
Combinatorial Residual Network: a decay-chain-aware classifier without explicit jet assignment
Oct 29, 2026John Alison, Patrick Bryant, Alejandro Gomez Espinosa +5High-Energy Physics
Department of Physics, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
Model-Agnostic Search for New Physics in the Presence of Imperfect Background by Smearing the Data
Oct 29, 2026Soheun Yi, John Alison, Mikael KuuselaHigh-Energy Physics Model Misspecification
Department of Statistics and Data Science, Carnegie Mellon University · Department of Physics, Carnegie Mellon University
SuperJEPA: Pretraining with a Supervision-Augmented Joint-Embedding Predictive Architecture for High Energy Physics
Oct 26, 2026Dylan Sheldon Rankin, Ho Fung TsoiSelf-Supervised Pre-Training Joint-Embedding Predictive Architecture
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA
The proton structure one mode at a time: a Neural Tangent Kernel analysis of a DIS fit
Oct 26, 2026Amedeo Chiefa, Luigi Del Debbio, Richard KenwayNeural Network Training Dynamics Neural Tangent Kernel
The Higgs Centre for Theoretical Physics, School of Physics and Astronomy, The University of Edinburgh, Peter Guthrie Tait Road, Edinburgh EH9 3FD, United Kingdom
LLMs Can Tackle High Energy Theoretical Physics, yet Gaps Remain: Part I, Quantum Field Theory
Oct 23, 2026Tong Liu, Bohan Li, Gongrui Cheng +3Scientific Reasoning in Language Models Language Model Reasoning Evaluation
Ludwig-Maximilians-Universität München, München, Germany · Munich Center for Machine Learning · Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan +3
Track finding and particle identification performance with particle physics foundation models
Oct 22, 2026Shuhang Li, Haiwang Yu, Gabor Galgoczi +9Trajectory Reconstruction High-Energy Physics
Columbia University, New York, NY 10027, USA · Brookhaven National Laboratory, Upton, NY 11973, USA · State University of New York, Stony Brook, New York, NY 11794, USA +1
How Far Can Foundation Models Be Distilled for Efficient Jet Tagging?
Oct 21, 2026Jay Agarwal, Siddharth Khare, Dhruv KumarJet Tagging Knowledge Distillation
BITS Pilani, KEK · BITS Pilani
This is SParTA: A modular, efficient and scalable transformer architecture for jet-tagging
Oct 19, 2026Rajneil Baruah, Subhadeep Mondal, Sunando Kumar Patra +1Transformer Jet Tagging
Department of Physics, SEAS, Bennett University, Greater Noida, Uttar Pradesh, 201310, India · Department of Physics, Bangabasi Evening College, Kolkata, 700009, West Bengal, India
Machine-learning-based neutrino-event generation constrained by experimental measurements
Oct 14, 2026Jose Luis Bonilla, Krzysztof M. Graczyk, Rwik Dharmapal Banerjee +3High-Energy Physics Constrained Generative Modeling
Institute of Theoretical Physics, University of Wrocław, plac Maxa Borna 9, 50-204, Wrocław, Poland
Ergodic topology sampling toward the continuum with Stochastic Normalizing Flows
Oct 14, 2026Claudio Bonanno, Andrea Bulgarelli, Elia Cellini +4Lattice Field Theory High-Energy Physics
Albert Einstein Center for Fundamental Physics, Institute for Theoretical Physics, University of Bern, Sidlerstraße 5, CH-3012 Bern, Switzerland · Transdisciplinary Research Area “Building Blocks of Matter and Fundamental Interactions” (TRA Matter) and Helmholtz Institute for Radiation and Nuclear Physics (HISKP), University of Bonn, Nussallee 14-16, 53115 Bonn, Germany · Higgs Centre for Theoretical Physics, School of Physics and Astronomy, The University of Edinburgh, Edinburgh EH9 3FD, United Kingdom +3
Data Free Training of Diffusion Models for Lattice Field Theories
Oct 13, 2026Dennis Grinwald, Ankur Singha, Khaled Kahouli +2Lattice Field Theory Diffusion Model Sampling
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 +2Lattice Field Theory High-Energy Physics
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 KomijaniLattice Field Theory High-Energy Physics
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 PaicSequence-to-Sequence Models High-Energy Physics
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

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Reinforcement Learning in High-Energy Physics: A Survey from Accelerator Control to Theory Exploration

Luca Anzalone

Independent researcher

Reinforcement Learning RL Control High-Energy Physics

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Reinforcement learning (RL) is a machine learning paradigm in which an agent learns a decision-making policy by interacting with an environment, guided by a scalar reward rather than explicit supervision. Supervised and unsupervised learning have become indispensable tools in high-energy physics (HEP), but they learn from fixed datasets, whereas many HEP tasks are closed-loop decisions: the data an algorithm observes depend on its choices, the feedback is sparse or delayed, and the objective is often measured on a real machine or in a stochastic simulation, not by a differentiable model. This survey reviews the application of RL to such tasks: accelerator control and feedback, data quality monitoring, jet grooming, clustering, and representation, charged particle tracking, beyond-the-Standard-Model (BSM) and string model building, detector design, trigger control, analysis, and formal quantum field theory computation. It provides a self-contained introduction to the underlying RL methods, from model-free and model-based to multi-agent, offline, inverse, and bandit approaches, reviews each domain against its traditional methods, and summarizes the head-to-head comparisons. The applications range from policies deployed on real accelerators and evaluated on recorded collision data to exploratory studies in formal theory. RL is most advantageous for repeated tasks with a fast simulator, decisions inside fast control loops, operation under faults and corrupted measurements, physics-based rewards in place of labels, and sequential discrete constructions with a reusable policy, while Bayesian optimization, genetic algorithms, and differentiable methods remain competitive elsewhere. We conclude with the open challenges that recur across domains, including reward design, sim-to-real transfer, safety, the lack of shared benchmarks, and the statistical validity of policy-driven decisions.