Nuclear Data

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2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Learning Nuclear Structure with AI: Radii and Collectivity
Sep 15, 2026Giuliano Giacalone, Sokratis Trifinopoulos, Mike WilliamsNuclear Data Collective Behaviors
Theoretical Physics Department, CERN, CH-1211 Geneva 23, Switzerland · Physik-Institut, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland · Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208, USA +2
Inclusive electron-nucleus cross section models from domain adaptation
Sep 8, 2026Krzysztof M. Graczyk, Beata E. Kowal, Rwik Dharmapal Banerjee +3Nuclear Data Transfer Learning
Institute of Theoretical Physics, University of Wroclaw, plac Maxa Borna 9, 50-204, Wroclaw, Poland
Panda Diplomacy: Foundation Model Pre-training across Particle Imaging Detectors for High Energy and Nuclear Physics
Sep 1, 2026Samuel Young, César Jesús-Valls, Kazuhiro TeraoWireless Foundation Models Foundation Model
Stanford University · Stanford, CA 94305 · European Organization for Nuclear Research (CERN) +3
Learning to Resolve Neutron Resonances with Fully Convolutional Neural Networks
Jul 27, 2026Nataly R. Panczyk, Athanasios Stamatopoulos, Josef Svoboda +1Nuclear Data Raman
Dept. of Nuclear Engineering and Radiological Sciences University of Michigan, Ann Arbor, MI 48109 · Physics Division, Los Alamos National Laboratory Los Alamos, NM 87545 · Dept. of Computer Science and Engineering University of Michigan, Ann Arbor, MI 48109
Bridging Ab Initio Symmetries and Global Nuclear Masses with Interpretable Neural Networks
Jun 26, 2026Phong Dang, Evander Espinoza, Xiaoliang Wan +6Nuclear Data Symmetry
Department of Physics and Astronomy, Louisiana State University, Baton Rouge, 70803, LA, USA · Department of Mathematics, Louisiana State University, Baton Rouge, 70803, LA, USA · Quantum CodeX, Baton Rouge, 70808, LA, USA +4
Ultra-Peripheral Collisions as a Nuclear-Structure Interferometer with Interpretable Multitask Deep Learning
Jun 22, 2026Jing-Zong Zhang, Wang-Mei Zha, Lingxiao Wang +1Nuclear Data Cone-Beam Computed Tomography
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
Integrating Out, Twice:The Open-System Case That Neural-Network Ensemble Theory Is Missing
Jun 8, 2026Jin LeiNeural Tangent Kernel Nuclear Data
Department of Physics, Tongji University, Shanghai 200092, China · Southern Center for Nuclear-Science Theory (SCNT), Institute of Modern Physics, Chinese Academy of Sciences, Huizhou 516000, Guangdong Province, China
Product units in gated recurrent units improve nuclear-mass prediction
Jun 5, 2026Ziyuan Li, Paulo S. A. Freitas, John W. Clark +1Nuclear Data Recurrent Model
Department of Mathematics, Informatics and Technology, University of Applied Sciences Koblenz, Joseph-Rovan-Allee 2, 53424 Remagen, Germany · Technical University of Munich, Munich, Germany · Department of Mathematics, University of Madeira, Campus Universitário da Penteada, Funchal, 9020-105, Portugal +1
Inverse Critical Experiment Design via Gradient Optimization and a Multigroup Attention-Based Neural Network Architecture
Jun 1, 2026Will Savage, Logan Burnett, Dean PriceNuclear Fusion Generative Inverse Design
Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, 60 Vassar St., Cambridge, 02139, MA, US
Machine Learning methods for event classification and vertex reconstruction of the 12C + 12C reaction with the MATE-TPC
May 27, 2026Minghui Zhang, Xiaobin Li, Jie Chen +11Nuclear Data Reaction
College of Science, Southern University of Science and Technology, Shenzhen, 518055, China · Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China · School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China +1
ATOM: Instantiating Budget-Controllable Multi-Agent Collaboration via Nucleus-Electron Hierarchy
May 25, 2026Xinkui Zhao, Sai Liu, Yifan Zhang +6Collaboration Atoms
Zhejiang University · Ningbo Global Innovation Center, Zhejiang University · Zhejiang Key Laboratory of Digital-Intelligence Service Technology +1
Multi-Fidelity Learning with Shallow Recurrent Decoders for Reactor Physics
May 22, 2026Stefano Riva, Carolina Introini, J. Nathan Kutz +1Multi-Fidelity Nuclear Data
Autodesk Research, London, UK · Department of Energy, Nuclear Engineering Division, Politecnico di Milano, Milan, Italy · Department of Mechanical and Nuclear Engineering and Emirates Nuclear Technology Center, Khalifa University, Abu Dhabi (127788), United Arab Emirates
Neural Operators as Efficient Function Interpolators
May 8, 2026Vasilis Niarchos, Angelos Sirbu, Sokratis TrifinopoulosFourier Neural Operators Neural Operators
Department of Physics, CCTP and ITCP, University of Crete, 71303, Greece · Theoretical Physics Department, CERN, Geneva, Switzerland · Physik-Institut, Universität Zürich, 8057 Zürich, Switzerland
BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulation
May 7, 2026Richard Hildebrandt, Evangelos Kourlitis, Baran Hashemi +7Nuclear Data Physics Simulation
Technical University of Munich · Max-Planck Institute for Mathematics in the Sciences, Leipzig, Germany · SLAC National Accelerator Laboratory +3
ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics
Apr 1, 2026Zeyu Xia, Tyler Kim, Trevor Reed +3Physics-Informed Learning 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
LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole-Slide Images
Jan 6, 2026Matěj Pekár, Vít Musil, Rudolf Nenutil +2Whole-Slide Images Computational Pathology
Masaryk University, Faculty of Informatics, Botanická 68a, Brno, 602 00, Czech Republic · Masaryk Memorial Cancer Institute, Žlutý kopec 7, Brno, 656 53, Czech Republic · BBMRI-ERIC, Neue Stiftingtalstraße 2/B/6, Graz, 8010, Austria
Quantitative Understanding of PDF Fits and their Uncertainties
Dec 30, 2025Amedeo Chiefa, Luigi Del Debbio, Richard KenwayNeural Tangent Kernel Parametric Physics-Informed Neural Network
The Higgs Centre for Theoretical Physics, School of Physics and Astronomy, The University of Edinburgh, Peter Guthrie Tait Road, Edinburgh EH9 3FD, United Kingdom

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Learning Nuclear Structure with AI: Radii and Collectivity

Giuliano Giacalone, Sokratis Trifinopoulos, Mike Williams

Theoretical Physics Department, CERN, CH-1211 Geneva 23, Switzerland · Physik-Institut, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland · Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208, USA+2

Nuclear Data Collective Behaviors

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Low-energy nuclear structure is encoded in a broad body of experimental information across the chart of nuclides. Learning how this information is organized across observables and nuclei can provide a data-driven empirical baseline for theoretical extrapolations and experimental design. Here, we develop held-out ensembles based on NuCLR (Nuclear Co-Learned Representations), a multi-task model of nuclear data, to study charge radii and electric-quadrupole transition strengths. Out-of-fold (OOF) validation shows that shared representation improves performance over single-task learning, yielding a charge-radius

\mathrm{RMS}

deviation of

0.0147~{\rm fm}

and a

\mathrm{B(E2)}

\mathrm{RMS}

deviation of

0.192~e^2{\rm b}^2

across hundreds of nuclides, competitive with state-of-the-art nuclear models. Our error bars estimate the expected prediction accuracy across the nuclear chart, highlighting regions where new data would encode information beyond the learned patterns. NuCLR thus serves as a data-driven surveyor of nuclear structure and a step toward a shared, multi-observable foundation model of the nuclear chart.

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