| KURAMITSU, Y., T. TAGUCHI, F. NIKAIDO, T. MINAMI, T. HIHARA, S. SUZUKI, K. ODA, K. KURAMOTO, T. YASUI, Y. ABE, K. IBANO, H. TAKABE, C. M. CHU, K. T. WU, W. Y. WOON, S. H. CHEN, C. S. JAO, Y. C. CHEN, Y. L. LIU, A. MORACE, A. YOGO, Y. ARIKAWA, H. KOHRI, A. TOKIYASU, S. KODAIRA, T. KUSUMOTO, M. KANASAKI, T. ASAI, Y. FUKUDA, K. KONDO, H. KIRIYAMA, T. HAYAKAWA, S. J. TANAKA, S. ISAYAMA, N. WATAMURA, H. SUZUKI, H. S. KUMAR, N. OHNISHI, T. PIKUZ, E. FILIPPOV, K. SAKAI, R. YASUHARA, M. NAKATA, R. ISHIKAWA, T. HOSHI, A. MIZUTA, Nima BOLOUKI, N. SAURA, S. BENKADDA, M. KOENIG and S. HAMAGUCHI. Optimization of laser-driven quantum beam generation and the applications with artificial intelligence. Physics of Plasmas. AIP Publishing, 2024, vol. 31, No 5, p. 1-12. ISSN 1070-664X. Available from: https://dx.doi.org/10.1063/5.0190062. |
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@article{2408603,
author = {Kuramitsu, Y. and Taguchi, T. and Nikaido, F. and Minami, T. and Hihara, T. and Suzuki, S. and Oda, K. and Kuramoto, K. and Yasui, T. and Abe, Y. and Ibano, K. and Takabe, H. and Chu, C. M. and Wu, K. T. and Woon, W. Y. and Chen, S. H. and Jao, C. S. and Chen, Y. C. and Liu, Y. L. and Morace, A. and Yogo, A. and Arikawa, Y. and Kohri, H. and Tokiyasu, A. and Kodaira, S. and Kusumoto, T. and Kanasaki, M. and Asai, T. and Fukuda, Y. and Kondo, K. and Kiriyama, H. and Hayakawa, T. and Tanaka, S. J. and Isayama, S. and Watamura, N. and Suzuki, H. and Kumar, H. S. and Ohnishi, N. and Pikuz, T. and Filippov, E. and Sakai, K. and Yasuhara, R. and Nakata, M. and Ishikawa, R. and Hoshi, T. and Mizuta, A. and Bolouki, Nima and Saura, N. and Benkadda, S. and Koenig, M. and Hamaguchi, S.},
article_number = {5},
doi = {http://dx.doi.org/10.1063/5.0190062},
keywords = {Convolutional neural network; Artificial intelligence; Artificial neural networks; Machine learning; Astrophysics; Graphene; Spectroscopy; Tracking devices; Lasers; Plasma turbulence},
language = {eng},
issn = {1070-664X},
journal = {Physics of Plasmas},
title = {Optimization of laser-driven quantum beam generation and the applications with artificial intelligence},
url = {https://pubs.aip.org/aip/pop/article/31/5/053108/3295205/Optimization-of-laser-driven-quantum-beam},
volume = {31},
year = {2024}
}
TY - JOUR
ID - 2408603
AU - Kuramitsu, Y. - Taguchi, T. - Nikaido, F. - Minami, T. - Hihara, T. - Suzuki, S. - Oda, K. - Kuramoto, K. - Yasui, T. - Abe, Y. - Ibano, K. - Takabe, H. - Chu, C. M. - Wu, K. T. - Woon, W. Y. - Chen, S. H. - Jao, C. S. - Chen, Y. C. - Liu, Y. L. - Morace, A. - Yogo, A. - Arikawa, Y. - Kohri, H. - Tokiyasu, A. - Kodaira, S. - Kusumoto, T. - Kanasaki, M. - Asai, T. - Fukuda, Y. - Kondo, K. - Kiriyama, H. - Hayakawa, T. - Tanaka, S. J. - Isayama, S. - Watamura, N. - Suzuki, H. - Kumar, H. S. - Ohnishi, N. - Pikuz, T. - Filippov, E. - Sakai, K. - Yasuhara, R. - Nakata, M. - Ishikawa, R. - Hoshi, T. - Mizuta, A. - Bolouki, Nima - Saura, N. - Benkadda, S. - Koenig, M. - Hamaguchi, S.
PY - 2024
TI - Optimization of laser-driven quantum beam generation and the applications with artificial intelligence
JF - Physics of Plasmas
VL - 31
IS - 5
SP - 1-12
EP - 1-12
PB - AIP Publishing
SN - 1070664X
KW - Convolutional neural network
KW - Artificial intelligence
KW - Artificial neural networks
KW - Machine learning
KW - Astrophysics
KW - Graphene
KW - Spectroscopy
KW - Tracking devices
KW - Lasers
KW - Plasma turbulence
UR - https://pubs.aip.org/aip/pop/article/31/5/053108/3295205/Optimization-of-laser-driven-quantum-beam
N2 - We have investigated space and astrophysical phenomena in nonrelativistic laboratory plasmas with long high-power lasers, such as collisionless shocks and magnetic reconnections, and have been exploring relativistic regimes with intense short pulse lasers, such as energetic ion acceleration using large-area suspended graphene. Increasing the intensity and repetition rate of the intense lasers, we have to handle large amounts of data from the experiments as well as the control parameters of laser beamlines. Artificial intelligence (AI) such as machine learning and neural networks may play essential roles in optimizing the laser and target conditions for efficient laser ion acceleration. Implementing AI into the laser system in mind, as the first step, we are introducing machine learning in ion etch pit analyses detected on plastic nuclear track detectors. Convolutional neural networks allow us to analyze big ion etch pit data with high precision and recall. We introduce one of the applications of laser-driven ion beams using AI to reconstruct vector electric and magnetic fields in laser-produced turbulent plasmas in three dimensions.
ER -
KURAMITSU, Y., T. TAGUCHI, F. NIKAIDO, T. MINAMI, T. HIHARA, S. SUZUKI, K. ODA, K. KURAMOTO, T. YASUI, Y. ABE, K. IBANO, H. TAKABE, C. M. CHU, K. T. WU, W. Y. WOON, S. H. CHEN, C. S. JAO, Y. C. CHEN, Y. L. LIU, A. MORACE, A. YOGO, Y. ARIKAWA, H. KOHRI, A. TOKIYASU, S. KODAIRA, T. KUSUMOTO, M. KANASAKI, T. ASAI, Y. FUKUDA, K. KONDO, H. KIRIYAMA, T. HAYAKAWA, S. J. TANAKA, S. ISAYAMA, N. WATAMURA, H. SUZUKI, H. S. KUMAR, N. OHNISHI, T. PIKUZ, E. FILIPPOV, K. SAKAI, R. YASUHARA, M. NAKATA, R. ISHIKAWA, T. HOSHI, A. MIZUTA, Nima BOLOUKI, N. SAURA, S. BENKADDA, M. KOENIG and S. HAMAGUCHI. Optimization of laser-driven quantum beam generation and the applications with artificial intelligence. \textit{Physics of Plasmas}. AIP Publishing, 2024, vol.~31, No~5, p.~1-12. ISSN~1070-664X. Available from: https://dx.doi.org/10.1063/5.0190062.
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