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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  1. Searching for New Physics with Reinforcement Learning

    Sep 9, 2026Jacky Kumar, Marianne Bouchard, David LondonHigh-Energy Physics

    Physique des Particules, Universit´e de Montr´eal, 1375 Avenue Th´er`ese-Lavoie-Roux, Montr´eal, QC H2V 0B3, Canada

  2. "Transforming" LHCb: self-supervised maps of heavy-flavour decays

    Sep 8, 2026Marko Stamenkovic, Greg LandsbergTransformerSelf-Supervised Learning

    Department of Physics, Brown University, Providence, Rhode Island 02912, U.S.A.

  3. Flexible Spectral-Normalized Neural Gaussian Process for Dynamic Aperture Prediction

    Sep 8, 2026Yousra El-Bachir, Frederik Van der Veken, Davide di Croce +4Neural Surrogate ModelingUncertainty Quantification

    Swiss Data Science Center, ETH Zurich & EPFL · CERN, Geneva · EPFL, Lausanne; CERN, Geneva

  4. Inclusive electron-nucleus cross section models from domain adaptation

    Sep 8, 2026Krzysztof M. Graczyk, Beata E. Kowal, Rwik Dharmapal Banerjee +3Fine-TuningDomain Adaptation

    Institute of Theoretical Physics, University of Wroclaw, plac Maxa Borna 9, 50-204, Wroclaw, Poland

  5. Likelihood-Based Unsupervised Anomaly Detection in CMS Dijet Events

    Sep 6, 2026Bhavishya Chebrolu, Hitesh Rasineni, Prajwal Aaryan ImmadiAnomaly DetectionUnsupervised Anomaly Detection

    VIT-AP University, Amaravati, India · Mohan Babu University, Tirupati, 517102, India · VIT-AP University, Amaravati, 522241, India

  6. A Differentiable Neural Surrogate for Photon Propagation in Neutrino Telescopes

    Sep 4, 2026Felix J. Yu, Berthy T. Feng, Nicholas Kamp +1Neural Surrogate ModelingPhysics-Based Simulation

    Harvard University · NSF IAIFI · Massachusetts Institute of Technology

  7. Panda Diplomacy: Foundation Model Pre-training across Particle Imaging Detectors for High Energy and Nuclear Physics

    Sep 1, 2026Samuel Young, César Jesús-Valls, Kazuhiro TeraoSelf-Supervised Pre-TrainingPoint Cloud Learning

    Stanford University · Stanford, CA 94305 · European Organization for Nuclear Research (CERN) +3

  8. Comparing Classical and Quantum Machine Learning for Regression in High Energy Physics Collision Data

    Aug 28, 2026Tariq Mahmood, Zain ul Abidin, Itzel Luviano Soto +1Quantum Machine LearningQuantum Neural Networks

    Centre for High Energy Physics, University of the Punjab, Lahore, Pakistan. · Facultad de Ingenier´ıa Civil, Universidad Michoacana de San Nicol´as de Hidalgo, Morelia, Michoac´an, M´exico · Facultad de Ingenier´ıa El´ectrica, Universidad Michoacana de San Nicol´as de Hidalgo, Morelia, Michoac´an, M´exico +1

  9. Learning Transverse Momentum Distributions from Raw Scattering Events via Conditional Diffusion

    Aug 27, 2026Jitao Xu, Christopher Cocuzza, Kevin Braga +3High-Energy PhysicsConditional Distribution Estimation

    Department of Computer Science, Old Dominion University · Department of Physics, William & Mary · Thomas Jefferson National Accelerator Facility

  10. Finding and using interpretable latents in a neutrino foundation model with sparse autoencoders

    Aug 26, 2026Raphaël Bonnet-Guerrini, Johann Ioannou-Nikolaides, Inar Timiryasov +1Mechanistic InterpretabilityUncertainty Quantification

    Computer Science Department, Università degli Studi di Milano, Milano, Italy · INFN Sezione di Milano, Milano, Italy · Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark

  11. Classical and Hybrid Quantum Machine Learning for Trigger-Like Event Selection on CMS Open Data: An Eight-Qubit, PCA-Constrained Benchmark

    Aug 26, 2026Tariq Mahmood, Muhammad Awais Rafique, Talab Hussain +3Hybrid Quantum-Classical MLHigh-Energy Physics

    Centre for High Energy Physics, University of the Punjab, Lahore, Pakistan. · Facultad de Ciencias Físico-Matemáticas, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Michoacán, Mexico. · Facultad de Ingeniería Eléctrica, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Michoacán, Mexico.

  12. S-matrix informed neural networks for amplitude analysis

    Aug 24, 2026Wyatt A. Smith, Arkaitz Rodas, Marius D. Thomas +5Physics-Informed MLHigh-Energy Physics

    Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA · Department of Physics, University of California, Berkeley, California 94720, USA · Department of Physics, The College of William & Mary, Williamsburg, Virginia 23187, USA +7

  13. Neural Network Field Theory at Finite Width

    Aug 21, 2026Christian Ferko, Aaron MutchlerHigh-Energy Physics

    Department of Physics, Northeastern University, Boston, MA 02115, USA · The NSF Institute for Artificial Intelligence and Fundamental Interactions

  14. What Neural Network Field Theory Can and Cannot Realise on a Computer

    Aug 21, 2026Thomas R. HarveyHigh-Energy Physics

    The NSF AI Institute for Artificial Intelligence and Fundamental Interactions. Center for Theoretical Physics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.

  15. Hadronic Mono-Z Dark Matter Sensitivity with Flow Matching on CMS Open Data

    Aug 21, 2026Hitesh Rasineni, Bhavishya ChebroluHigh-Energy Physics

    VIT-AP University, Amaravati, India · VIT-AP University, Amaravati, 522241, India · Mohan Babu University, Tirupati, India +1

  16. CDRL: Certification-Driven Reinforcement Learning for Neutrino Flavor Model Discovery

    Aug 21, 2026Piyush Jha, Jake Rudolph, Victoria Knapp-Pérez +3Reinforcement LearningConstrained RL

    School of Computer Science, Georgia Institute of Technology, USA · School of Physics, Georgia Institute of Technology, USA · University of California, Irvine, USA +3

  17. Functional anatomy of Pythia-Herwig differences with Kolmogorov-Arnold networks

    Aug 16, 2026Arghya ChattopadhyayHigh-Energy Physics

    Department of Physics, University of Puerto Rico at Mayagüez Mayagüez, Puerto Rico 00681, USA

  18. Uncovering Hidden Leptonic Correlations with Flow Matching and Autoencoders

    Aug 15, 2026Haruto Kitagawa, Satsuki Nishimura, Hajime OtsukaHigh-Energy Physics

    Department of Physics, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan · Department of Physics, Kyoto University, Kitashirakawa-Oiwakecho, Sakyo-ku, Kyoto 606-8502, Japan · Quantum and Spacetime Research Institute (QuaSR), Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan

  19. Spinning Conformal Correlators from Neural Networks

    Aug 15, 2026Manas Dogra, James Halverson, Joydeep NaskarHigh-Energy Physics

    Department of Physics, Northeastern University, Boston, MA, 02115, USA · The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, Cambridge, MA, U.S.A. · Beijing Institute of Mathematical Sciences and Applications, Huaibei Town, Huairou District, Beijing 101408, China

  20. Pairton: Iterative Reconstruction of Short-Lived Particles

    Aug 14, 2026Andreas Hermansen, Chris Scheulen, Tobias GollingHigh-Energy PhysicsConditional Generative Modeling

    Département de physique nucléaire et corpusculaire, University of Geneva

  21. Contrastive Learning for Interpretable Anomaly Detection at Collider Experiments

    Aug 13, 2026Haoyi Jia, Sagar Addepalli, Julia GonskiAnomaly DetectionInterpretable Anomaly Detection

    Department of Physics, Stanford University, 450 Jane Stanford Way, Stanford, CA 94305 · SLAC National Accelerator Laboratory, 2575 Sand Hill Rd, Menlo Park, CA 94025

  22. Fine-tuned Normalizing Flows for ALICE Zero Degree Calorimeter Fast Simulation

    Aug 13, 2026Emilia Majerz, Jacek Otwinowski, Witold Dzwinel +1Neural Surrogate ModelingNormalizing Flows

    AGH University of Krakow, Poland · The Henryk Niewodniczański Institute of Nuclear Physics, Polish Academy of Sciences

  23. Exploring new directions in enhancing the ACTS parameter optimization suite

    Aug 11, 2026Chance LaVoie, Qi Bin Lei, Rocky Bala Garg +1Hyperparameter OptimizationBayesian Optimization

    Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA · Department of Physics, University of California, Santa Cruz, and Santa Cruz Institute for Particle Physics, 1156 High St., Santa Cruz, CA 95064, USA · Department of Physics, Stanford University, Stanford, CA 94305, USA

  24. A Lightweight Foundation Model for Collider Physics with Multi-Domain Adaptation

    Jul 29, 2026Liangyu Wu, Qibin Liu, Alexander Yue +1Self-Supervised Pre-TrainingHigh-Energy Physics

    Department of Physics, Stanford University, 450 Jane Stanford Way, Stanford, 94305, CA, USA. · SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, 94025, CA, USA.

  25. Normalizing Flows to Reconstruct Pseudo-PDFs

    Jul 28, 2026Yamil Cahuana Medrano, Kostas OrginosNormalizing FlowsBayesian Inverse Problems

    Department of Physics William & Mary Williamsburg, VA 23185

  26. Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliders

    Jul 27, 2026Gregorio de la Fuente, Jesse ThalerJet TaggingHigh-Energy Physics

    Center for Theoretical Physics – a Leinweber Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States · The NSF AI Institute for Artificial Intelligence and Fundamental Interactions · Institut des Hautes ´Etudes Scientifiques, 91440 Bures-sur-Yvette, France +1

  27. Predict before you train: Scaling Laws for particle physics foundation models

    Jul 25, 2026Jan-Lucas Uslu, Benjamin Nachman, Christopher ReHigh-Energy PhysicsAI Scaling Laws

    Department of Applied Physics, Stanford University, Stanford, CA 94305, USA · Fundamental Physics Directorate, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA · Department of Physics, Stanford University, Stanford, CA 94305, USA +1

  28. Neural solutions of coupled ghost and gluon Dyson--Schwinger equations in Landau gauge

    Jul 23, 2026Rodrigo Carmo TerinHigh-Energy Physics

    King Juan Carlos University, Faculty of Experimental Sciences and Technology, Department of Applied Physics, Av. del Alcalde de Móstoles, 28933 Madrid, Spain

  29. Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments

    Jul 22, 2026Ivan Ge, Sagar Addepalli, Abhilasha Dave +1Online Anomaly DetectionQuantum Machine Learning

    Department of Physics, Stanford University, 450 Jane Stanford Way, Stanford, 94305, CA, USA. · SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, 94025, CA, USA.

  30. Machine Can Automatically Discover Parametric Functions to Model HEP Data

    Jul 22, 2026Ho Fung Tsoi, Dylan Rankin, Cecile Caillol +5Automated Algorithm DiscoveryHigh-Energy Physics

    University of Pennsylvania, USA · European Organization for Nuclear Research (CERN), Switzerland · University of Cambridge, UK +4