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Big Data 2019 American Journal of Computer Science and Information Technology
ISSN: 2349-3917 Page 25
March 04-05, 2019
Barcelona, Spain
8th Edition of International Conference on
Big Data &
Data Science
The development of the machine learning in recent years has
begun to benefit the fundamental physics research. In the
neutrino detector KamLAND aiming to unravel the mysteries of
the universe, discriminating gamma-ray that inhibits the signal
has been ultimate task. This research made it possible by using
recurrent neural networks (RNN).
Recent Publications
1. A. Gando et al. “Search for Majorana Neutrinos Near the
Inverted Mass Hierarchy Region with KamLAND-Zen”,
Physical Review Letters 117, p.082503 (2016)
2. A. Gando et al. “A Search for electron antineutrinos
associated with gravitational wave events GW150914
and GW151226 using KamLAND”, The Astrophysical
Journal Letters, Volume 829, Number 2 (2016)
3. A. Gando et al. “Search for double-beta decay of 136Xe
to excited states of 136Ba with the KamLAND-Zen
experiment”, Nuclear Physics A, Volume 946 p.171-181
(2016).
Biography
Shingo Hayashida is a research fellow of the Japan Society for the Promo-
tion of Science (JSPS). He is expected to take PhD fromTohoku University in
Japan in March 2019. He has published 6 papers in reputed journals.
Application of the RNN in the fundamental
physics with KamLAND experiment
Shingo Hayashida
Tohoku University, Japan
Shingo Hayashida, Am J Compt Sci Inform Technol 2019, Volume 7
DOI: 10.21767/2349-3917-C1-009