Tokyo Metropolitan University
Tokyo Metropolitan University
Gunma University
Department of Interdisciplinary Space Science, Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency (JAXA)(ISAS)
Department of Interdisciplinary Space Science, Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency (JAXA)(ISAS)
Kanagawa University
Aoyama gakuin University
Aoyama gakuin University
出版者
宇宙航空研究開発機構(JAXA)
出版者（英）
Japan Aerospace Exploration Agency (JAXA)
雑誌名
宇宙航空研究開発機構研究開発報告: 宇宙科学情報解析論文誌: 第11号
雑誌名(英)
JAXA Research and Development Report: Journal of Space Science Informatics Japan: Volume 11
The General AntiParticle Spectrometer (GAPS) aims to search for antideuterons originating from dark matter through highly sensitive observation of cosmic ray antiparticles. In GAPS, the use of machine learning is being considered for cosmic ray antiparticle identification, which requires high rejection power and identification accuracy. In a previous study, a three-dimensional convolutional neural network (CNN) model was used to achieve high rejection power for simulated data under limited conditions where the incident angle and position of the antiparticle are fixed. In this study, in order to seek the explanatory power of discrimination by analyzing the factors that contribute to the high accuracy of machine learning, the parts that the CNN model focuses on in discrimination are visualized, and the cases of incorrect discrimination are analyzed.
内容記述
形態: カラー図版あり
内容記述（英）
Physical characteristics: Original contains color illustrations
宇宙線反粒子識別を対象とした機械学習の応用と根拠の可視化
AA2130033004.pdf
(1.07MB)
