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.

Self-Supervised ConvLSTM for Fermi Large Area Telescope Transient Detection
May 21, 2026Alberto Garinei, Stefano Speziali, Alessandro Vispa +11Convolutional LSTM High-Energy Physics
Idea-Re S.r.l., 06125, Perugia, Italy · bDipartimento Scienze Ingegneristiche, Università Guglielmo Marconi, 00193, Rome, Italy · cIstituto Nazionale Fisica Nucleare, sezione di Perugia, 06123, Perugia, Italy +1
Patch Hierarchical Attention Transformer for Efficient Particle Jet Tagging
May 20, 2026Aaron Wang, Zihan Zhao, Alan Xia +5Jet Tagging Efficient Attention
University of Illinois Chicago Chicago, IL 60607, USA · University of California San Diego La Jolla, CA 92093, USA · California Institute of Technology Pasadena, CA 91125, USA +1
Probing SMEFT Operators through $t\bar{t}t\bar{t}$ Production with Hyper-Graph Neural Networks at the LHC
May 18, 2026Amir Subba, Sanmay GangulyHigh-Energy Physics
Wilczek Quantum Center, Shanghai Institute for Advanced Studies, Shanghai 201315, China University of Science and Technology of China, Hefei 230026, China · Department of Physics, Indian Institute of Technology Kanpur, Uttar Pradesh 208016, India
ML-based Fast Simulation of FARICH Responses
May 17, 2026Foma Shipilov, Alexander Barnyakov, Artem Ivanov +1High-Energy Physics Conditional Generative Modeling
HSE University, Moscow, Russia · Budker Institute of Nuclear Physics SB RAS, Novosibirsk, Russia · Novosibirsk State Technical University, Novosibirsk, Russia +1
Noise scheduling and linear dynamics in diffusion models on Lie groups
May 17, 2026Javad KomijaniLie Group Methods High-Energy Physics
Institute for Theoretical Physics, ETH Zurich, 8093 Zurich, Switzerland
Operator-Guided Model Reduction for Generative Sampling in Lattice Field Theory
May 11, 2026Moxian QianFlow Matching Markov Chain Monte Carlo
Johannes Gutenberg University Mainz, 55128 Mainz, Germany
From raw data to neutrino candidates: a neural-network pipeline for Baikal-GVD
May 11, 2026A. Matseiko, G. Plotnikov, I. KharukHigh-Energy Physics
Institute for Nuclear Research of the Russian Academy of Sciences · Moscow Institute of Physics and Technology · Institute for Nuclear Research of the Russian Academy of Sciences, 60th October Anniversary Prospect 7a, Moscow, 117312, Russia +1
Reconfigurable Computing Challenge: Real-Time Graph Neural Networks for Online Event Selection in Big Science
May 11, 2026Marc Neu, Frank Baptist, Thomas Lobmaier +3High-Energy Physics FPGA-Based Neural Network Acceleration
Institute for Information Processing Technology, Karlsruhe Institute of Technology, Germany · Institute of Experimental Particle Physics, Karlsruhe Institute of Technology, Germany
Dissecting Jet-Tagger Through Mechanistic Interpretability
May 11, 2026Saurabh Rai, Sanmay GangulyTransformer Interpretability Attention Head Analysis
Department of Physics, Indian Institute of Technology, Kanpur. Uttar-Pradesh 208016, India
Transfer Learning Across Fast- and Full-Simulation Domains in High-Energy Physics
May 8, 2026Matthias Schott, Lucie FlekDomain Adaptation High-Energy Physics
Institute of Physics, University of Bonn, Germany · Bonn-Aachen International Center for Information Technology (b-it), University of Bonn, Germany
Uncovering Hidden Systematics in Neural Network Models for High Energy Physics
May 8, 2026Lucie Flek, Philipp Alexander Jungs, Akbar Karimi +6Uncertainty Quantification Adversarial Robustness
Bonn-Aachen Institute of Technology, University of Bonn, Germany · Physics Institute III A, RWTH Aachen University, Germany · Institute of Physics, University of Bonn, Germany +1
Learning Minimal-Deviation Corrections for Multi-Dimensional Mismodelling in HEP Simulations
May 8, 2026Matthias Schott, Lucie FlekDistribution Matching High-Energy Physics
Institute of Physics, University of Bonn, Germany · Bonn-Aachen International Center for Information Technology (b-it), University of Bonn, Germany
Diffusion model for SU(N) gauge theories
May 7, 2026Javad Komijani, Marina K. Marinkovic, Lara TurgutScore Matching Diffusion Sampling
Institute for Theoretical Physics, ETH Zurich, 8093 Zurich, Switzerland
From Experimental Limits to Physical Insight: A Retrieval-Augmented Multi-Agent Framework for Interpreting Searches Beyond the Standard Model
May 4, 2026Altan Cakir, Ayca YerlikayaMultimodal RAG Multi-Agent LLM Systems
Istanbul Technical University, Department of Data Science and Analytics, Istanbul, Turkey · Istanbul Technical University, Department of Physics Engineering, Istanbul, Turkey
HepScript: A Dual-Use DSL for Human-AI Collaborative Data Analysis Workflows in High-Energy Physics
May 2, 2026Junkun Jiao, Tong Liu, Ke Li +6Scientific Code Generation High-Energy Physics
Jilin University, Changchun, Jilin, China · Institute of High Energy Physics, CAS, Beijing, China · University of Chinese Academy of Sciences, Beijing, China
Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories
Apr 22, 2026Ali Rayat, Yaohang Li, Gia-Wei ChernMessage Passing Neural Networks High-Energy Physics
Department of Physics, University of Virginia, Charlottesville, VA 22904, USA · Department of Computer Science, Old Dominion University, Norfolk, VA 22904, USA.
Neural posterior estimation of the neutrino direction in IceCube using transformer-encoded normalizing flows on the sphere
Apr 21, 2026R. Abbasi, M. Ackermann, J. Adams +411Normalizing Flows Transformer
RL-ABC: Reinforcement Learning for Accelerator Beamline Control
Apr 21, 2026Anwar Ibrahim, Fedor Ratnikov, Maxim Kaledin +2RL Control High-Energy Physics
Application of a Mixture of Experts-based Foundation Model to the GlueX DIRC Detector
Apr 17, 2026Cristiano Fanelli, James Giroux, Cole Granger +1High-Energy Physics Mixture-of-Experts Models
William & Mary, Department of Data Science, Williamsburg, VA 23185, USA · William & Mary, Department of Physics, Williamsburg, VA 23185, USA
Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics
Apr 17, 2026Abhishek Sawaika, Durga Pritam Suggisetti, Udaya Parampalli +1Hybrid Quantum-Classical ML High-Energy Physics
qCLOUDS Lab, School of Computing and Information Systems, The University of Melbourne, Australia · Birla Institute of Technology and Science Pilani, Dubai, UAE
Machine learning for four-dimensional SU(3) lattice gauge theories
Apr 14, 2026Urs WengerHigh-Energy Physics
Institute for Theoretical Physics Albert Einstein Center for Fundamental Physics University of Bern CH-3012 Bern Switzerland
Cross-Domain Transfer with Particle Physics Foundation Models: From Jets to Neutrino Interactions
Apr 14, 2026Gregor Krzmanc, Vinicius Mikuni, Benjamin Nachman +1Cross-Domain Transfer Learning High-Energy Physics
Department of Physics, Stanford University, Stanford, CA 94305, USA · Nagoya University, Kobayashi-Maskawa Institute, Aichi 464-8602, Japan · Fundamental Physics Directorate, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA +1
High-dimensional inference for the $γ$ -ray sky with differentiable programming
Apr 9, 2026Siddharth Mishra-Sharma, Tracy R. Slatyer, Yitian Sun +1Bayesian Inference High-Energy Physics
Faculty of Computing and Data Sciences, Boston University, Boston, MA 02215, USA · 2The NSF AI Institute for Artificial Intelligence and Fundamental Interactions · Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA +2
Lecture notes on Machine Learning applications for global fits
Apr 8, 2026Jorge AldaSurrogate Modeling High-Energy Physics
Dipartimento di Fisica e Astronomia “Galileo Galilei”, Università degli Studi di Padova and INFN Sezione Padova, via Marzolo 8 35129 Padova, Italy. · Centro de Astropartículas y Física de Altas Energías (CAPA), Pedro Cerbuna 12 50009 Zaragoza, Spain.
Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pre-training
Apr 8, 2026Saúl Alonso-Monsalve, Fabio Cufino, Umut Kose +2Self-Supervised Pre-Training High-Energy Physics
IPA, ETH Zürich, Otto Stern Weg 5, Zurich, 8093, Switzerland.
Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approach
Apr 8, 2026Emre Gurkanli, Michael SpannowskyAnomaly Detection Autoencoders
Department of Physics, Sinop University, Turkiye · Institute for Theoretical Physics, Campus Süd, Karlsruhe Institute of Technology (KIT), D-76128 Karlsruhe, Germany · Institute for Quantum Materials and Technologies, Karlsruhe Institute of Technology, Karlsruhe 76131, Germany
Adversarial Robustness of Time-Series Classification for Crystal Collimator Alignment
Apr 7, 2026Xaver Fink, Borja Fernandez Adiego, Daniele Mirarchi +4Adversarial Robustness Adversarial Attacks
CERN, Geneva, Switzerland · RWTH Aachen University, Aachen, Germany · DESY, Hamburg, Germany
PhaseFlow4D: Physically Constrained 4D Beam Reconstruction via Feedback-Guided Latent Diffusion
Apr 4, 2026Alexander Scheinker, Alexander Plastun, Peter Ostroumov4D Reconstruction Physics-Informed Diffusion Models
Los Alamos National Laboratory Los Alamos 87545, NM, USA · Facility for Rare Isotopes Beams Michigan State University, East Lansing, MI 48824, USA
Generative models on phase space
Apr 2, 2026Zachary Bogorad, Ibrahim Elsharkawy, Yonatan Kahn +2Generative Modeling Constrained Generative Modeling
Theory Division, Fermi National Accelerator Laboratory, Batavia, IL, USA · Department of Physics, University of Toronto and Vector Institute, Toronto, ON, Canada and NERSC, Lawrence Berkeley National Laboratory, Berkeley, California, USA · Department of Physics, University of Toronto and Vector Institute, Toronto, ON, Canada +2
Topological Effects in Neural Network Field Theory
Apr 2, 2026Christian Ferko, James Halverson, Vishnu Jejjala +1High-Energy Physics Phase Transitions
The NSF Institute for Artificial Intelligence and Fundamental Interactions (IAIFI) and Department of Physics, Northeastern University, Boston, MA 02115, USA · Mandelstam Institute for Theoretical Physics, School of Physics, and NITheCS, University of the Witwatersrand, Johannesburg, WITS 2050, South Africa · Institute of Physics, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Netherlands

May 21, 2026J/K move · Enter open · S save

Self-Supervised ConvLSTM for Fermi Large Area Telescope Transient Detection

Alberto Garinei, Stefano Speziali, Alessandro Vispa, Andrea Marini+10

Idea-Re S.r.l., 06125, Perugia, Italy · bDipartimento Scienze Ingegneristiche, Università Guglielmo Marconi, 00193, Rome, Italy · cIstituto Nazionale Fisica Nucleare, sezione di Perugia, 06123, Perugia, Italy+1

Convolutional LSTM High-Energy Physics Reconstruction-Based Anomaly Detection Time-Series Anomaly Detection

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We present a framework for detecting transient gamma-ray phenomena in a controlled environment by combining end-to-end simulations of the Fermi-LAT sky with self-supervised spatio-temporal deep learning. We generate a ten-year synthetic Universe with gtobssim and process the simulated events into daily all-sky maps of counts and exposure, obtaining a time-ordered sequence that mirrors the structure of Fermi-LAT observations. To model the nominal evolution of the sky, we employ a Convolutional Long Short-Term Memory (ConvLSTM) network that operates directly on map sequences, preserving spatial locality while learning temporal dependencies. The model is trained to reconstruct expected emission, and departures from the learned baseline are quantified through pixel-wise mean-squared residual maps. We then define statistically motivated anomaly criteria by estimating per-pixel thresholds from the residual distribution on the training set, and we enforce spatial coherence via local filtering to suppress isolated fluctuations. The ConvLSTM is then deployed as trained predictor on Fermi-LAT daily maps, where the sky can depart from the nominal behavior because of genuine astrophysical variability and instrumental non-stationarities. The resulting pipeline flags localized, time-dependent excesses consistent with high-variable sources or transient events (e.g., flares or GRBs) and provides a benchmark for evaluating anomaly-detection strategies on long-duration, Fermi-LAT-like datasets.