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.

ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics
Apr 1, 2026Zeyu Xia, Tyler Kim, Trevor Reed +3Surrogate Modeling Conditional Flow Matching
Department of Computer Science, University of Virginia, 85 Engineer’s Way, Charlottesville, VA 22904, USA · School of Data Science, University of Virginia, 1919 Ivy Road, Charlottesville, VA 22903, USA · Department of Physics, Indiana University Bloomington, 727 East Third Street, Bloomington, IN 47405, USA
Suppression of $^{14}\mathrm{C}$ photon hits in large liquid scintillator detectors via spatiotemporal deep learning
Mar 29, 2026Junle Li, Zhaoxiang Wu, Guanda Gong +5High-Energy Physics
School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China · Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China · School of Physical Sciences, University of Chinese Academy of Science, Beijing 100049, China +1
Hardware-Aware Tensor Networks for Real-Time Quantum-Inspired Anomaly Detection at Particle Colliders
Mar 27, 2026Sagar Addepalli, Prajita Bhattarai, Abhilasha Dave +1Anomaly Detection Tensor Networks
SLAC National Accelerator Laboratory, 2575 Sand Hill Rd, Menlo Park, 94025, CA, USA.
Vision Transformers and Graph Neural Networks for Charged Particle Tracking in the ATLAS Muon Spectrometer
Mar 26, 2026Jonathan RenuschHigh-Energy Physics Graph Neural Networks
on behalf of the ATLAS Collaboration · CERN, Geneva, Switzerland
Calorimeter Shower Superresolution with Conditional Normalizing Flows: Implementation and Statistical Evaluation
Mar 26, 2026Andrea CossoHigh-Energy Physics Image Super-Resolution
Università degli Studi di Genova Scuola di Scienze Matematiche, Fisiche e Naturali Laurea Magistrale in Fisica
B-jet Tagging Using a Hybrid Edge Convolution and Transformer Architecture
Mar 22, 2026Diego F. Vasquez Plaza, Vidya ManianJet Tagging High-Energy Physics
Department of Electrical and Computer Engineering, University of Puerto Rico Mayaguez, PR 00681-9000, USA
Neutrino Oscillation Parameter Estimation Using Structured Hierarchical Transformers
Mar 21, 2026Giorgio Morales, Gregory Lehaut, Antonin Vacheret +2Parameter Estimation High-Energy Physics
Normandie Univ, UNICAEN, ENSICAEN, CNRS, GREYC. Caen, France · Normandie Univ, ENSICAEN, UNICAEN, CNRS/IN2P3, LPC Caen. Caen, France
AI Agents Can Already Autonomously Perform Experimental High Energy Physics
Mar 20, 2026Eric A. Moreno, Samuel Bright-Thonney, Andrzej Novak +2Autonomous Research Agents AI Agents for Scientific Discovery
Department of Physics, Massachusetts Institute of Technology · NSF AI Institute for Artificial Intelligence and Fundamental Interactions · CERN
Shapes are not enough: CONSERVAttack and its use for finding vulnerabilities and uncertainties in machine learning applications
Mar 14, 2026Philip Bechtle, Lucie Flek, Philipp Alexander Jung +7Adversarial Robustness Adversarial Perturbations
Institute of Physics, University of Bonn, Germany. · Bonn-Aachen Institute of Technology, University of Bonn, Germany. · Physics Institute III A, RWTH Aachen University, Germany. +1
Learning to Unscramble: Simplifying Symbolic Expressions via Self-Supervised Oracle Trajectories
Mar 11, 2026David ShihSymbolic Reasoning Self-Supervised Learning
NHETC, Dept. of Physics and Astronomy, Rutgers University, Piscataway, NJ 08854, USA
First Estimation of Model Parameters for Neutrino-Induced Nucleon Knockout Using Simulation-Based Inference
Mar 10, 2026Karla Tame-Narvaez, Steven Gardiner, Aleksandra Ćiprijanović +1Parameter Estimation Model Calibration
Fermi National Accelerator Laboratory, Batavia, Illinois 60510 USA · Department of Astronomy and Astrophysics, University of Chicago, Chicago, IL 60637 · NSF-Simons AI Institute for the Sky (SkAI), 172 E. Chestnut St., Chicago, IL 60611
Characterization and upgrade of a quantum graph neural network for charged particle tracking
Mar 9, 2026Matteo Argenton, Laura Cappelli, Concezio BozziQuantum Neural Networks High-Energy Physics
Department of Physics and Earth Science, University of Ferrara, Via Saragat 1, Ferrara, 44122, Italy · INFN Ferrara, Via Saragat 1, Ferrara, 44122, Italy
Agentic AI -- Physicist Collaboration in Experimental Particle Physics: A Proof-of-Concept Measurement with LEP Open Data
Mar 5, 2026Anthony Badea, Yi Chen, Marcello Maggi +2AI Agents for Scientific Discovery Data Analysis Agents
University of Chicago, Enrico Fermi Institute, 60637 IL, USA · Vanderbilt University, Nashville, Tennessee, USA · Istituto Nazionale di Fisica Nucleare, Bari Division, BA 70126, Italy +1
End-to-end event reconstruction for precision physics at future colliders
Mar 4, 2026Dolores Garcia, Lena Herrmann, Gregor Krzmanc +1High-Energy Physics
European Center for Nuclear Research (CERN), Geneva 1211, Switzerland · Stanford University, Stanford, CA 94305, USA
High-Resolution Image Reconstruction with Unsupervised Learning and Noisy Data Applied to Ion-Beam Dynamics for Particle Accelerators
Mar 4, 2026Francis Osswald, Mohammed Chahbaoui, Xinyi LiangImage Reconstruction Image Inverse Problems
IPHC · IPHC, CNRS Nuclei & Particules, Univ. of Strasbourg, 67000, Strasbourg, France · UNISTRA +3
Variance reduction in lattice QCD observables via normalizing flows
Mar 3, 2026Ryan Abbott, Denis Boyda, Yang Fu +5Variance Reduction High-Energy Physics
Physics Department, Columbia University, New York, NY 10027, USA · Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · The NSF AI Institute for Artificial Intelligence and Fundamental Interactions +3
GNN For Muon Particle Momentum estimation
Mar 3, 2026Vishak K Bhat, Eric A. F. Reinhardt, Sergei GleyzerHigh-Energy Physics Graph Neural Networks
IIT Dhanbad, India · Department of Physics and Astronomy, The University of Alabama, USA
A Monte Carlo estimator of flow fields for sampling and noise problems
Feb 27, 2026Michael S. Albergo, Gurtej KanwarHigh-Energy Physics Flow Map Learning
Society of Fellows, Harvard University 78 Mount Auburn St, Cambridge, MA United States · Higgs Centre for Theoretical Physics, School of Physics and Astronomy, University of Edinburgh, EH9 3FD Edinburgh, United Kingdom
Reconstruction of cosmic-ray direction and energy in radio arrays using deep ensemble graph neural networks
Feb 26, 2026Arsène Ferrière, Aurélien Benoit-Lévy, Olivier Martineau-Huynh +1Graph Neural Networks Direction-of-Arrival Estimation
Université Paris-Saclay, CEA, List, F-91120 Palaiseau, France · Sorbonne Université, CNRS, Laboratoire de Physique Nucléaire et des Hautes Énergies (LPNHE), 4 Pl. Jussieu, 75005 Paris, France · National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China +2
Solving stiff dark matter equations via Jacobian Normalization with Physics-Informed Neural Networks
Feb 25, 2026M. P. Bento, H. B. Câmara, J. R. Rocha +1Physics-Informed ML Dynamical Systems
Departamento de Física and CFTP, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal · Institute of Experimental and Applied Physics, Czech Technical University in Prague, Prague 160 00, Czech Republic
PhyGHT: Physics-Guided HyperGraph Transformer for Signal Purification at the HL-LHC
Feb 24, 2026Mohammed Rakib, Luke Vaughan, Shivang Patel +3Physics-Informed GNNs High-Energy Physics
Department of Computer Science Oklahoma State University, USA · Department of Physics Oklahoma State University, USA
Neural Scaling Laws for Boosted Jet Tagging
Feb 17, 2026Matthias Vigl, Nicole Hartman, Michael Kagan +1Neural Scaling Laws AI Scaling Laws
Technical University of Munich · SLAC National Accelerator Laboratory
Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml
Feb 17, 2026Katya Govorkova, Julian Garcia Pardinas, Vladimir Loncar +5FPGA-Based Neural Network Acceleration Efficient Neural Network Inference
Massachusetts Institute of Technology (MIT), 77 Massachusetts Ave, Cambridge, MA 02139, USA · Department of Experimental Physics, European Organization for Nuclear Research (CERN), 1211 Geneva 23, Switzerland · University of Milano-Bicocca (Italy), and Department of Experimental Physics, European Organization for Nuclear Research (CERN), 1211 Geneva 23, Switzerland
Learning the S-matrix from data: Rediscovering gravity from gauge theory via symbolic regression
Feb 16, 2026Nathan MoynihanHigh-Energy Physics Symbolic Regression
Queen Mary, University of London, Mile End Road, London E1 4NS, UK
DCTracks: An Open Dataset for Machine Learning-Based Drift Chamber Track Reconstruction
Feb 16, 2026Qian Liyan, Zhang Yao, Yuan Ye +11High-Energy Physics
Institute of High Energy Physics, Chinese Academy of Sciences No.19B Yuquan Road, Shijingshan, Beijing, China · University of Chinese Academy of Sciences No.19A Yuquan Road, Shijingshan, Beijing, China · Sun Yat-sen University School of Science, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China +3
Comparison of Image Processing Models in Quark Gluon Jet Classification
Jan 29, 2026Daeun Kim, Jaeyoon Cho, Jiwon Lee +5Vision Transformer High-Energy Physics
Department of Physics, Inha University, Incheon, Republic of Korea
Learning to bin: differentiable and Bayesian optimization for multi-dimensional discriminants in high-energy physics
Jan 12, 2026Johannes Erdmann, Nitish Kumar Kasaraguppe, Florian MausolfHigh-Energy Physics Differentiable Optimization
RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany
Quantitative Understanding of PDF Fits and their Uncertainties
Dec 30, 2025Amedeo Chiefa, Luigi Del Debbio, Richard KenwayUncertainty Quantification High-Energy Physics
The Higgs Centre for Theoretical Physics, School of Physics and Astronomy, The University of Edinburgh, Peter Guthrie Tait Road, Edinburgh EH9 3FD, United Kingdom
KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging
Dec 8, 2025Md Raqibul Islam, Adrita Khan, Mir Sazzat Hossain +6Graph Representation Learning Jet Tagging
Center for Computational & Data Sciences, Independent University, Bangladesh, Dhaka-1229, Bangladesh · Department of Theoretical Physics, University of Dhaka, Dhaka-1000, Bangladesh · Department of Physical Sciences, Independent University, Bangladesh, Dhaka-1229, Bangladesh
Domain-Specific Agents for Cherenkov Telescope Array Control Software and Gamma-Ray Data Analysis
Oct 1, 2025Dmitriy Kostunin, Elisa Jones, Vladimir Sotnikov +3High-Energy Physics LLM Agents
Deutsches Elektronen-Synchrotron DESY, 15738 Zeuthen, Germany · JetBrains Limited, 8046 Paphos, Cyprus · JetBrains GmbH, 80639 München, Germany +1

Apr 1, 2026J/K move · Enter open · S save

ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics

Zeyu Xia, Tyler Kim, Trevor Reed, Judy Fox+2

Department of Computer Science, University of Virginia, 85 Engineer’s Way, Charlottesville, VA 22904, USA · School of Data Science, University of Virginia, 1919 Ivy Road, Charlottesville, VA 22903, USA · Department of Physics, Indiana University Bloomington, 727 East Third Street, Bloomington, IN 47405, USA

Surrogate Modeling Conditional Flow Matching High-Energy Physics

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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. While Conditional Flow Matching (CFM) offers a robust acceleration approach, we demonstrate its standard training loss is fundamentally misleading. Specifically, utilizing a Jefferson Lab Nuclear Physics (NP) kinematic dataset (

γp \to ρ^0 p \to π^+π^- p

), we expose that CFM loss plateaus prematurely, obscuring ongoing physical refinement. To verify this disconnect is a dataset-agnostic pathology, we introduce ScatterPrism, an efficient generative surrogate evaluated against both the NP data and synthetic stress tests modeling challenging 1D distribution topologies. Coupling these benchmarks, we establish that physics-informed metrics continue improving long after standard loss converges. Consequently, we propose a multi-metric diagnostic protocol to ensure true kinematic fidelity without data memorization. Driven by NP challenges relevant to the forthcoming Electron-Ion Collider (EIC), this unified machinery has strong potential to extend to High-Energy Physics (HEP) applications, such as jet modeling. Furthermore, the framework holds promise for broader domains requiring rigorous generative reliability, including medical imaging, astrophysics, and quantitative finance.