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Cited article:
Georg Schnabel
EPJ Nuclear Sci. Technol., 4 (2018) 33
Published online: 2018-11-14
This article has been cited by the following article(s):
6 articles
Parameter optimisation using Bayesian inference for spallation models
J. Hirtz, J.-C. David, J. Cugnon, I. Leya, J. L. Rodríguez-Sánchez and G. Schnabel The European Physical Journal A 60 (7) (2024) https://doi.org/10.1140/epja/s10050-024-01370-y
G-HyND: a hybrid nuclear data estimator with Gaussian processes
Hiroki Iwamoto, Osamu Iwamoto and Satoshi Kunieda Journal of Nuclear Science and Technology 59 (3) 334 (2022) https://doi.org/10.1080/00223131.2021.1971120
Experiment-based determination of the excitation function for the production of
Ti44
in proton-irradiated vanadium samples
M. Veicht, I. Kajan, J.-C. David, et al. Physical Review C 104 (1) (2021) https://doi.org/10.1103/PhysRevC.104.014615
Conception and Software Implementation of a Nuclear Data Evaluation Pipeline
G. Schnabel, H. Sjöstrand, J. Hansson, et al. Nuclear Data Sheets 173 239 (2021) https://doi.org/10.1016/j.nds.2021.04.007
Generation of nuclear data using Gaussian process regression
Hiroki Iwamoto Journal of Nuclear Science and Technology 57 (8) 932 (2020) https://doi.org/10.1080/00223131.2020.1736202
Spallation, cosmic rays, meteorites, and planetology
J.-C. David and I. Leya Progress in Particle and Nuclear Physics 109 103711 (2019) https://doi.org/10.1016/j.ppnp.2019.103711