American Journal of Computer Science and Information Technology

Application of the RNN in the fundamental physics with KamLAND experiment

8^th Edition of International Conference on Big Data & Data Science
March 04-05, 2019 Barcelona, Spain

Shingo Hayashida

Tohoku University, Japan

Posters & Accepted Abstracts: Am J Compt Sci Inform Technol

DOI: 10.21767/2349-3917-C1-009

Abstract

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 Promotion of Science (JSPS). He is expected to take PhD from Tohoku University in Japan in March 2019. He has published 6 papers in reputed journals.

E-mail: h.shingo@awa.tohoku.ac.jp