Related papers: FENDL: A library for fusion research and applicati…
The ENDF/B-VIII.1 library is the newest recommended evaluated nuclear data file by the Cross Section Evaluation Working Group (CSEWG) for use in nuclear science and technology applications, and incorporates advances made in the six years…
High quality nuclear data is the most fundamental underpinning for all neutron metrology applications. This paper describes the release of version II of the International Reactor Dosimetry and Fusion File (IRDFF-II) that contains a…
Recent multi-messenger detection of the binary neutron star merger (GW170817) energized the astrophysical community and encouraged further research for determination of nuclear physics observables. Comprehensive studies of atomic nuclei in…
Neural network interatomic potentials (NNPs) have recently proven to be powerful tools to accurately model complex molecular systems while bypassing the high numerical cost of ab-initio molecular dynamics simulations. In recent years,…
The neutron-induced fission cross sections were simultaneously evaluated for the JENDL-5 library for $^{233,235}$U and $^{239,241}$Pu from 10 keV to 200 MeV and for $^{238}$U and $^{240}$Pu from 100 keV to 200 MeV. Evaluation was performed…
The activities of the EUROfusion consortium on the development of high quality nuclear data for fusion applications include evaluations of deuteron induced reactions and related data libraries for needs of the DEMO fusion power plant and…
Cross section data are fundamental quantities which affect the accuracy of all calculations in nuclear applications. A new dosimetry library IRDFF-II that contains cross section evaluations with full uncertainty quantification was developed…
Photo-induced reaction cross section data are of importance for a variety of current or emerging applications, such as radiation shielding design and radiation transport analyses, calculations of absorbed dose in the human body during…
The International Network of Nuclear Reaction Data Centres (NRDC) coordinated by the IAEA Nuclear Data Section (NDS) is successfully collaborating in the maintenance and development of the EXFOR library. As the scope of published data…
Aluminum is a crucial material in the nuclear industry, valued for its ability to perform reliably over time. The manuscript focuses on validating aluminum neutron transport libraries. The validation was conducted by activating samples in…
We introduce a novel method for studying systematic trends in nuclear reaction data using generative adversarial networks. Libraries of nuclear cross section evaluations exhibit intricate systematic trends across the nuclear landscape, and…
We describe evaluation methods that make use of experimental data, and nuclear model calculations, to develop an ENDF-formatted data library for the reaction p + Li7 for incident protons with energies up to 150 MeV. The important…
Federated learning (FL) is a rapidly growing research field in machine learning. However, existing FL libraries cannot adequately support diverse algorithmic development; inconsistent dataset and model usage make fair algorithm comparison…
Fission fragment yield evaluations are one of the important nuclear data studies. Fission accompanies various physical observables such as prompt fission neutron, prompt fission gamma, and delayed-neutrons. When evaluating fission fragment…
An accurate description of interactions between thermal neutrons (below 4 eV) and materials is key to simulate the transport of neutrons in a wide range of applications such as criticality-safety, reactor physics, compact accelerator-driven…
In this work, we explore the use of an iterative Bayesian Monte Carlo (IBM) procedure for nuclear data evaluation within a Talys Evaluated Nuclear data Library (TENDL) framework. In order to identify the model and parameter combinations…
Accurate modeling of neutron-induced (n,p) reaction cross sections is essential for diverse applications in nuclear physics, including reactor design, nuclear astrophysics, and radionuclide production. However, experimental data are often…
With the popularity of the deep neural network (DNN), hardware accelerators are demanded for real time execution. However, lengthy design process and fast evolving DNN models make hardware evaluation hard to meet the time to market need.…
Spectra emitted from ionized nebulae typically contain collisionally excited and recombination lines, which can be used to trace physical conditions and chemical abundances of the interstellar medium in our Galaxy and other galaxies.…
Federated Learning (FL) is an emerging learning scheme that allows different distributed clients to train deep neural networks together without data sharing. Neural networks have become popular due to their unprecedented success. To the…