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Iwamoto, Hiroki
JAEA-Conf 2021-001, p.83 - 87, 2022/03
We have developed a method to generate nuclear data using Gaussian process regression [1], which is one of the machine learning technique. This method generates nuclear data by treating measured data as the training data in machine learning. Since Gaussian process regression is based on nonparametric Bayesian inference, the generated nuclear data are expressed as a predictive distribution including uncertainty information. In this presentation, the basics of the Gaussian process model, some examples of the application to nuclear data generation, and other related topics will be presented. [1] H. Iwamoto, "Generation of nuclear data using Gaussian process regression", Journal of Nuclear Science and Technology, 50:8, 932-938 (2020).
Konno, Chikara; Kochiyama, Mami; Hayashi, Hirokazu
JAEA-Conf 2021-001, p.132 - 137, 2022/03
A SCALE6.2 ORIGEN library was produced with the AMPX-6 code from JENDL Activation Cross Section File for Nuclear Decommissioning 2017 (JENDL/AD-2017). For validation of the libraries, JPDR activation calculation was performed with ORIGEN and the libraries, which demonstrated the library had no problem.
Iwamoto, Nobuyuki
JAEA-Conf 2021-001, p.126 - 131, 2022/03
Rovira Leveroni, G.; Iwamoto, Osamu; Kimura, Atsushi; Nakamura, Shoji; Iwamoto, Nobuyuki; Endo, Shunsuke; Katabuchi, Tatsuya*; Terada, Kazushi*; Kodama, Yu*; Nakano, Hideto*; et al.
JAEA-Conf 2021-001, p.156 - 161, 2022/03
Nakayama, Shinsuke
JAEA-Conf 2021-001, p.65 - 70, 2022/03
Since deuteron is a weakly bound system consisting of a proton and a neutron, it easily breaks up and emits a neutron through interaction with a target nucleus. Utilizing this property, intensive neutron sources using deuteron accelerators have been proposed for not only science and engineering fields but also medical applications. For design studies of such facilities, accurate and comprehensive nuclear data of deuteron-induced reactions are indispensable. Toward evaluation of deuteron nuclear data, we have developed a code system dedicated for deuteron-induced reactions, called DEURACS. In DEURACS, breakup processes of incident deuteron are taken into account. DEURACS was so far successfully applied to analyses of production of nucleons, composite particles up to 
= 4, and residual nuclei. In this talk, we will present the results of these analyses and discuss how important it is to consider the breakup processes for accurate prediction of deuteron-induced reactions. Moreover, we have recently developed JENDL/DEU-2020, a deuteron nuclear database for 
Li, 
Be, and 
C up to 200 MeV. DEURACS was employed for evaluation of JENDL/DEU-2020. Validation of JENDL/DEU-2020 was carried out by the simulation with the Monte Carlo transport codes. These validation results will also be presented.
Iwamoto, Yosuke; Yoshida, Makoto*; Meigo, Shinichiro; Yonehara, Katsuya*; Ishida, Taku*; Nakano, Keita; Abe, Shinichiro; Iwamoto, Hiroki; Spina, T.*; Ammigan, K.*; et al.
JAEA-Conf 2021-001, p.138 - 143, 2022/03
To predict the operating lifetime of materials in high-energy radiation environments at proton accelerator facilities, Monte Carlo code are used to calculate the number of displacements per atom (dpa). However, there is no experimental data in the energy region above 30 GeV. In this presentation, we introduce our experimental plan for displacement cross sections with 120-GeV protons at Fermilab Test Beam Facility. Experiments will be performed for the US fiscal year 2022. We developed the sample assembly with four wire sample of Al, Cu, Nb and W with 250-
m diameter and 4-cm length. The sample assembly will be maintained at around 4 K by using a cryocooler in a vacuum chamber. Then, changes in the electrical resistivity of samples will be obtained under 120-GeV proton irradiation. Recovery of the accumulated defects through isochronal annealing, which is related to the defect concentration in the sample, will also be measured after the cryogenic irradiation.
Iwamoto, Hiroki
JAEA-Conf 2021-001, p.24 - 29, 2022/03
Various spallation reaction models have been developed for the use of neutronic and shielding design of high-energy accelerator facilities such as J-PARC and ADS. However, their complicated theory for the de-excitation process has made improving their prediction accuracy difficult. In particular, it has been pointed out that the conventional models underestimate the yield of the spallation products produced from the fission reaction. This work has thus aimed to model the probability was described using a simpler, systematic expression, and then confirmed to predict fission cross sections for various incident energies and target nuclei with improved accuracy [1]. In this presentation, we will present the description of our model and research results. [1] H. Iwamoto and S. Meigo, "Unified description of the fission probability for highly excited nuclei", Journal of Nuclear Science and Technology, 56:2, 160-171 (2019).
Matsumura, Taichi; Okumura, Keisuke; Fujita, Manabu*
JAEA-Conf 2021-001, p.144 - 149, 2022/03
We require reliable nuclear data that can appropriately evaluate the radiation characteristics of fuel debris for the purpose such as development of new sensors, non-destructive assay technologies and optimization of radiation shielding. In the past, even if different results were obtained depending on calculation codes, it was difficult to clarify what caused the differences. To overcome it, we have developed a new reliable code to calculate radiation decay and radioactive source spectra that can accurately treat with large amounts of nuclides and all decay modes in the decay data file. As the first step, we compared the photon spectra of fuel debris by using the recent decay data files: JENDL/DDF-2015, decay sub-libraries of ENDF/B-VIII.0 and JEFF-3.3. In the presentation, we will report requests for the modifications on the decay scheme and branching ratio of decay mode for the next JENDL decay data file.
Nakano, Hideto*; Katabuchi, Tatsuya*; Rovira Leveroni, G.*; Kodama, Yu*; Terada, Kazushi*; Kimura, Atsushi; Nakamura, Shoji; Endo, Shunsuke
JAEA-Conf 2021-001, p.166 - 170, 2022/03
Kodama, Yu*; Katabuchi, Tatsuya*; Rovira Leveroni, G.; Nakano, Hideto*; Terada, Kazushi*; Kimura, Atsushi; Nakamura, Shoji; Endo, Shunsuke
JAEA-Conf 2021-001, p.162 - 165, 2022/03
Lu target irradiated with 0.4-, 1.3-, 2.2-, and 3.0-GeV protonsTakeshita, Hayato; Meigo, Shinichiro; Matsuda, Hiroki; Iwamoto, Hiroki; Nakano, Keita; Watanabe, Yukinobu*; Maekawa, Fujio
JAEA-Conf 2021-001, p.207 - 212, 2022/03
Prediction of nuclide production of spallation products by high-energy proton injection plays a fundamental and important role in shielding design of high-intensity proton accelerator facilities such as accelerator driven nuclear transmutation system (ADS). Since the prediction accuracy of the nuclear reaction models used in the production quantity prediction simulation is insufficient, it is necessary to improve the nuclear reaction models. We have measured nuclide production cross sections for various target materials with the aim of acquiring experimental data and improving nuclear reaction models. In this study, 1.3-, 2.2- and 3.0-GeV proton beams were irradiated to Lu target, and nuclide production cross-section data were acquired by the activation method. The measured data were compared with several nuclear reaction models used in Monte Carlo particle transport calculation codes to grasp the current prediction accuracy and to study how the nuclear reaction model could be improved.
