Virtual Journal of Nuclear Astrophysics
Edited by
Honey Arora
, Michigan State University and
Fry Fang
, University of Notre Dame
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Results 1 - 5 of 5
Uncertainty quantification in the machine-learning inference from neutron star probability distribution to the equation of state
Virtual Journal - Volume 22, Issue 35 - article #17
Yuki Fujimoto
,
Kenji Fukushima
,
Syo Kamata,
,
Koichi Murase
Physical Review D
http://link.aps.org/doi/10.1103/PhysRevD.110.034035
Extensive Studies of the Neutron Star Equation of State from the Deep Learning Inference with the Observational Data Augmentation. (arXiv:2101.08156v1 [nucl-th])
Virtual Journal - Volume 19, Issue 4 - article #3
Yuki Fujimoto
,
Kenji Fukushima
,
Koichi Murase
http://arxiv.org/abs/2101.08156
Mapping neutron star data to the equation of state of the densest matter using the deep neural network. (arXiv:1903.03400v1 [nucl-th])
Virtual Journal - Volume 17, Issue 11 - article #4
Yuki Fujimoto
,
Kenji Fukushima
,
Koichi Murase
http://arxiv.org/abs/1903.03400
Methodology study of machine learning for the neutron star equation of state
Virtual Journal - Volume 16, Issue 31 - article #23
Yuki Fujimoto
,
Kenji Fukushima,
,
Koichi Murase
Physical Review D
http://link.aps.org/doi/10.1103/PhysRevD.98.023019
Methodology study of machine learning for the neutron star equation of state. (arXiv:1711.06748v1 [nucl-th])
Virtual Journal - Volume 15, Issue 47 - article #8
Yuki Fujimoto
,
Kenji Fukushima
,
Koichi Murase
http://arxiv.org/abs/1711.06748
Results 1 - 5 of 5
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