Recent improvements of the particle and heavy ion transport code system – PHITS version 3.33

References

• Sato T, Iwamoto Y, Hashimoto S, et al. Features of particle and heavy ion transport code system PHITS version 3.02. J Nucl Sci Technol. 2018;55(6):684–690. doi: 10.1080/00223131.2017.1419890
   Web of Science ®Google Scholar
• Iwase H, Niita K, Nakamura T. Development of general-purpose particle and heavy ion transport Monte Carlo code. J Nucl Sci Technol. 2002;39(11):1142–1151. doi: 10.1080/18811248.2002.9715305
   Web of Science ®Google Scholar
• Sato T, Niita K, Matsuda N, et al. Particle and heavy ion transport code system, PHITS, version 2.52. J Nucl Sci Technol. 2013;50(9):913–923. doi: 10.1080/00223131.2013.814553
   Web of Science ®Google Scholar
• Iwamoto Y, Sato T, Hashimoto S, et al. Benchmark study of the recent version of the PHITS code. J Nucl Sci Technol. 2017;54(5):617–635. doi: 10.1080/00223131.2017.1297742
   Web of Science ®Google Scholar
• Iwamoto Y, Hashimoto S, Sato T, et al. Benchmark study of particle and heavy-ion transport code system using shielding integral benchmark archive and database for accelerator-shielding experiments. J Nucl Sci Technol. 2022;59(5):665–675. doi: 10.1080/00223131.2021.1993372
   Web of Science ®Google Scholar
• Kuga N, Shiiba T, Sato T, et al. Experimental and computational verifications of the dose calculation accuracy of PHITS for high-energy photon beam therapy. J Nucl Sci Technol. in press. doi:10.1080/00223131.2023.2275737.
   Web of Science ®Google Scholar
• Iwamoto O, Iwamoto N, Kunieda S, et al. Japanese evaluated nuclear data library version 5: JENDL-5. J Nucl Sci Technol. 2023;60(1):1–60. doi: 10.1080/00223131.2022.2141903
   Web of Science ®Google Scholar
• Liamsuwan T, Nikjoo H. A Monte Carlo track structure simulation code for the full-slowing-down carbon projectiles of energies 1 keV u−1-10 MeV u−1 in water. Phys Med Biol. 2013;58(3):673–701. doi: 10.1088/0031-9155/58/3/673
   PubMed Web of Science ®Google Scholar
• Nikjoo H, Emfietzoglou D, Liamsuwan T, et al. Radiation track, DNA damage and response-a review. Rep Prog Phys. 2016;79(11):116601. doi: 10.1088/0034-4885/79/11/116601
   PubMed Web of Science ®Google Scholar
• Ogawa T, Hirata Y, Matsuya Y, et al. Development and validation of proton track-structure model applicable to arbitrary materials. Sci Rep. 2021;11(1):24401. doi: 10.1038/s41598-021-01822-1
   PubMed Web of Science ®Google Scholar
• Nakayama S, Iwamoto O, Watanabe Y, et al. JENDL/DEU-2020: deuteron nuclear data library for design studies of accelerator-based neutron sources. J Nucl Sci Technol. 2021;58(7):805–821. doi: 10.1080/00223131.2020.1870010
   Web of Science ®Google Scholar
• Nishitani T, Yoshihashi S, Ogawa K, et al. Neutron yield calculation of thin and thick d-D targets by using PHITS with frag data table. J Nucl Sci Technol. 2022;59(4):534–541. doi: 10.1080/00223131.2021.1981475
   Web of Science ®Google Scholar
• Watanabe Y, Kosako K, Kunieda S, et al. Status of JENDL high energy file. J Korean Phys Soc. 2011;59(2):1040–1045. doi: 10.3938/jkps.59.1040
   Web of Science ®Google Scholar
• Matsuda N, Kunieda S, Okamoto T, et al. Ace library of JENDL-4.0/HE. Prog Nucl Sci Technol. 2019;6:225–229. doi: 10.15669/pnst.6.225
   Google Scholar
• Shibata K, Iwamoto O, Nakagawa T, et al. JENDL-4.0: a new library for nuclear science and engineering. J Nucl Sci Technol. 2011;48(1):1–30. doi: 10.1080/18811248.2011.9711675
   Web of Science ®Google Scholar
• Boudard A, Cugnon J, David JC, et al. New potentialities of the Liege intranuclear cascade model for reactions induced by nucleons and light charged particles. Phys Rev C. 2013;87(1):014606. doi: 10.1103/PhysRevC.87.014606
   Web of Science ®Google Scholar
• Furihata S. Statistical analysis of light fragment production from medium energy proton-induced reactions. Nucl Instrum Meth B. 2000;171(3):251–258. doi: 10.1016/S0168-583X(00)00332-3
   Web of Science ®Google Scholar
• ACE-J50 website [cited 2023 Apr. 18]. Available from: https://rpg.jaea.go.jp/main/en/ACE-J50/index.html
   Google Scholar
• Strumia A, Vissani F. Precise quasielastic neutrino/nucleon cross-section. Phys Lett B. 2003;564(1–2):42–54. doi: 10.1016/S0370-2693(03)00616-6
   Web of Science ®Google Scholar
• Nakamura S, Sato T, Ando S, et al. Neutrino-deuteron reactions at solar neutrino energies. Nucl Phys A. 2002;707(3–4):561–576. doi: 10.1016/S0375-9474(02)00993-4
   Web of Science ®Google Scholar
• Iwamoto H, Meigo S. Unified description of the fission probability for highly excited nuclei. J Nucl Sci Technol. 2019;56(2):160–171. doi: 10.1080/00223131.2018.1539351
   Web of Science ®Google Scholar
• Ogawa T, Hashimoto S, Sato T, et al. Development of gamma de-excitation model for prediction of prompt gamma-rays and isomer production based on energy-dependent level structure treatment. Nucl Instrum Meth B. 2014;325:35–42. doi: 10.1016/j.nimb.2014.02.007
   Web of Science ®Google Scholar
• Ogawa T, Hashimoto S, Sato T. Development of PHITS nuclear deexcitation model EBITEM ver.2. J Nucl Sci Technol in press . doi: 10.1080/00223131.2023.2261932
   Web of Science ®Google Scholar
• Matsuya Y, Kai T, Sato T, et al. Verification of KURBUC-based ion track structure mode for proton and carbon ions in the PHITS code. Phys Med Biol. 2021;66(6):06NT02. doi: 10.1088/1361-6560/abe65e
   PubMed Web of Science ®Google Scholar
• Geissel H, Scheidenberger C, Malzacher P, et al. ATIMA website [cited 2023 Apr. 18]. Available from: http://web-docs.gsi.de/~weick/atima/
   Google Scholar
• Kai T, Yokoya A, Ukai M, et al. Cross sections, stopping powers, and energy loss rates for rotational and phonon excitation processes in liquid water by electron impact. Radiat Phys Chem. 2015;108:13–17. doi: 10.1016/j.radphyschem.2014.11.008
   Web of Science ®Google Scholar
• Matsuya Y, Kai T, Sato T, et al. Track-structure modes in particle and heavy ion transport code system (PHITS): application to radiobiological research. Int J Radiat Biol. 2022;98(2):148–157. doi: 10.1080/09553002.2022.2013572
   PubMed Web of Science ®Google Scholar
• Sakaki Y, Namito Y, Sanami T, et al. Implementation of muon pair production in PHITS and verification by comparing with the muon shielding experiment at SLAC. Nucl Instrum Meth A. 2020;977:164323. doi: 10.1016/j.nima.2020.164323
   Web of Science ®Google Scholar
• Tsai YS. Pair production and bremsstrahlung of charged leptons. Rev Mod Phys. 1974;46(4):815–851. doi: 10.1103/RevModPhys.46.815
   Web of Science ®Google Scholar
• Berger MJ, Coursey JS, Zucker MA, et al. Stopping-power & range tables for electrons, protons, and helium ions. NIST Standard Reference Database 124: National Institute of Standards and Technology; 2017. https://dx.doi.org/10.18434/T4NC7P
   Google Scholar
• Seltzer SM, Fernandez-Varea JM, Andreo P, et al. ICRU Report 90, key data for ionizing-radiation dosimetry: Measurement standards and applications. J ICRU. 2016;14:1–110.
   Google Scholar
• Hirayama H, Namito Y, Bielajew AF, et al. The EGS5 code system. SLAC-R-730 and KEK report 2005-8. USA and Japan: SLAC National Accelerator Laboratory and High Energy Accelerator Research Organization; 2005.
   Google Scholar
• Hashimoto S, Sato T. Estimation method of systematic uncertainties in Monte Carlo particle transport simulation based on analysis of variance. J Nucl Sci Technol. 2019;56(4):345–354. doi: 10.1080/00223131.2019.1585989
   Web of Science ®Google Scholar
• Sato T, Furusawa Y. Cell survival fraction estimation based on the probability densities of domain and cell nucleus specific energies using improved microdosimetric kinetic models. Radiat Res. 2012;178(4):341–356. doi: 10.1667/RR2842.1
   PubMed Web of Science ®Google Scholar
• Sato T, Hashimoto S, Inaniwa T, et al. Implementation of simplified stochastic microdosimetric kinetic models into PHITS for application to radiation treatment planning. Int J Radiat Biol. 2021;97(10):1450–1460. doi: 10.1080/09553002.2021.1956003
   PubMed Web of Science ®Google Scholar
• Nordlund K, Zinkle SJ, Sand AE, et al. Primary radiation damage: a review of current understanding and models. J Nucl Mater. 2018;512:450–479. doi: 10.1016/j.jnucmat.2018.10.027
   Web of Science ®Google Scholar
• Iwamoto Y, Meigo S, Hashimoto S. Estimation of reliable displacements-per-atom based on athermal-recombination-corrected model in radiation environments at nuclear fission, fusion, and accelerator facilities. J Nucl Mater. 2020;538:152261. doi: 10.1016/j.jnucmat.2020.152261
   Web of Science ®Google Scholar
• Sato T, Matsuya Y, Ogawa T, et al. Improvement of the hybrid approach between Monte Carlo simulation and analytical function for calculating microdosimetric probability densities in macroscopic matter. Phys Med Biol. 2023;68(15):155005. doi: 10.1088/1361-6560/ace14c
   Web of Science ®Google Scholar
• Kai T, Maekawa F, Kosako K, et al. DCHAIN-SP 2001: high energy particle induced radioactivity calculation code. JAERI-Data/code 2001-016. Japan: Japan Atomic Energy Research Institute; 2001.
   Google Scholar
• Ratliff HN, Matsuda N, Abe S, et al. Modernization of the DCHAIN-PHITS activation code with new features and updated data libraries. Nucl Instrum Meth B. 2020;484:29–41. doi: 10.1016/j.nimb.2020.10.005
   Web of Science ®Google Scholar
• Matthiä D, Berger T, Mrigakshi AI, et al. A ready-to-use galactic cosmic ray model. Adv Space Res. 2013;51(3):329–338. doi: 10.1016/j.asr.2012.09.022
   Web of Science ®Google Scholar
• Tylka AJ, Dietrich WF, editors. A new and comprehensive analysis of proton spectra in ground-level enhanced (GLE) solar particle events. The 31st International Cosmic Ray Conference; 2009; Poland: Universal Academy Press, Lodź; 2009.
   Google Scholar
• Sato T, Nagamatsu A, Ueno H, et al. Comparison of cosmic-ray environments on earth, moon, mars, and spacecraft using PHITS. Radiat Prot Dosim. 2018;180(1–4):146–149. doi: 10.1093/rpd/ncx192
   PubMed Web of Science ®Google Scholar
• Sawyer D, Vette J AP-8 trapped proton environment for solar maximum and solar minimum. Report 76-06. Greenbelt, Maryland: National Space Science Data Center; 1976.
   Google Scholar
• Heynderickx D, Quaghebeur B, Wera J, et al. New radiation environment and effects models in the European space agency’s space environment information system (SPENVIS). Space Weather. 2004;2(10):S10S03. doi: 10.1029/2004SW000073
   Web of Science ®Google Scholar
• Sato T Analytical model for estimating terrestrial cosmic ray fluxes nearly anytime and anywhere in the world: extension of PARMA/EXPACS. PLoS One. 2015;10(12):e0144679. doi: 10.1371/journal.pone.0144679
   PubMed Web of Science ®Google Scholar
• Sato T Analytical model for estimating the zenith angle dependence of terrestrial cosmic ray fluxes. PLoS One. 2016;11(8):e0160390. doi: 10.1371/journal.pone.0160390
   PubMed Web of Science ®Google Scholar
• Satoh D, Sato T, Shigyo N, et al. SCINFUL-QMD; Monte Carlo based computer code to calculate response function and detection efficiency of a liquid organic scintillator for neutron energies up to 3 GeV. JAEA-Data/code 2006-023. Japan: Japan Atomic Energy Agency; 2006.
   Google Scholar
• Satoh D, Sato T. Improvements in the particle and heavy-ion transport code system (PHITS) for simulating neutron-response functions and detection efficiencies of a liquid organic scintillator. J Nucl Sci Technol. 2022;59(8):1047–1060. doi: 10.1080/00223131.2021.2019622
   Web of Science ®Google Scholar
• Malins A, Machida M, Niita K, et al. Continuous energy adjoint transport for photons in PHITS. EPJ Web Conf. 2017;153:06001. doi: 10.1051/epjconf/201715306001
   Google Scholar
• Ohnishi S. Gxsview: geometry and cross section viewer for calculating radiation transport. Softwarex. 2021;14:100681. doi: 10.1016/j.softx.2021.100681
   Web of Science ®Google Scholar
• Furuta T, Koba Y, Hashimoto S, et al. Development of the DICOM-based Monte Carlo dose reconstruction system for a retrospective study on the secondary cancer risk in carbon ion radiotherapy. Phys Med Biol. 2022;67(14):145002. doi: 10.1088/1361-6560/ac7998
   Web of Science ®Google Scholar
• Sato T, Furuta T, Liu Y, et al. Individual dosimetry system for targeted alpha therapy based on PHITS coupled with microdosimetric kinetic model. EJNMMI Phys. 2021;8(1):4. doi: 10.1186/s40658-020-00350-7
   PubMed Web of Science ®Google Scholar
• Schuemann J, McNamara AL, Warmenhoven JW, et al. A new standard DNA damage (SDD) data format. Radiat Res. 2019;191(1):76–92. doi: 10.1667/RR15209.1
   PubMed Web of Science ®Google Scholar
• Matsuya Y, Kai T, Yoshii Y, et al. Modeling of yield estimation for DNA strand breaks based on Monte Carlo simulations of electron track structure in liquid water. J Appl Phys. 2019;126(12):124701. doi: 10.1063/1.5115519
   Web of Science ®Google Scholar
• Matsuya Y, Kai T, Parisi A, et al. Application of a simple DNA damage model developed for electrons to proton irradiation. Phys Med Biol. 2022;67(21):215017. doi: 10.1088/1361-6560/ac9a20
   Web of Science ®Google Scholar
• Zerkin VV, Pritychenko B. The experimental nuclear reaction data (EXFOR): Extended computer database and web retrieval system. Nucl Instrum Meth A. 2018;888:31–43. doi: 10.1016/j.nima.2018.01.045
   Web of Science ®Google Scholar
• Hirata Y, Kai T, Ogawa T, et al. Implementation of the electron track-structure mode for silicon into PHITS for investigating the radiation effects in semiconductor devices. Jpn J Appl Phys. 2022;61(10):106004. doi: 10.35848/1347-4065/ac8ae9
   Web of Science ®Google Scholar
• Hirata Y, Kai T, Ogawa T, et al. Development of an electron track-structure mode for arbitrary semiconductor materials in PHITS. Jpn J Appl Phys. 2023;62(10): 106001. doi:10.35848/1347-4065/ad00f4
   Web of Science ®Google Scholar