Identification of infrared-ring structures by convolutional neural network

Shota Ueda; Shinji Fujita; Atsushi Nishimura; Toshikazu Onishi; Yoshito Shimajiri; Yusuke Miyamoto; Kazufumi Torii; Atsushi M. Ito; Shunya Takekawa; Hiroyuki Kaneko; Daisuke Yoshida; Taro Matsuo; Tsuyoshi Inoue; Yasutomo Kawanishi; Kazuki Tokuda

doi:10.1117/12.2560830

13 December 2020 Identification of infrared-ring structures by convolutional neural network

Shota Ueda, Shinji Fujita, Atsushi Nishimura, Toshikazu Onishi, Yoshito Shimajiri, Yusuke Miyamoto, Kazufumi Torii, Atsushi M. Ito, Shunya Takekawa, Hiroyuki Kaneko, Daisuke Yoshida, Taro Matsuo, Tsuyoshi Inoue, Yasutomo Kawanishi, Kazuki Tokuda

Author Affiliations +

Proceedings Volume 11452, Software and Cyberinfrastructure for Astronomy VI; 114522L (2020) https://doi.org/10.1117/12.2560830
Event: SPIE Astronomical Telescopes + Instrumentation, 2020, Online Only

Conference Poster

Abstract

Recently, the amount of data obtained from astronomical instruments has been increasing explosively, and data science methods such as Machine Learning/Deep Learning gain attention on the back of the growth in demand for automatic analysis. Using these methods, the number of applications to the target sources that have clear boundaries with the background i.e., stars, planets, and galaxies is increasing year by year. However, there are a few studies which applied the data science methods to the interstellar medium (ISM) distributed in the Galactic plane, which have complicated and ambiguous silhouettes. We aim to develop classifiers to automatically extract various structures of the ISM by Convolutional Neural Network (CNN) that is strong in image recognition even in deep learning. In this study, we focus on the infra-red (IR) ring structures distributed in the Galactic plane. Based on the catalog of Churchwell et al. (2006, 2007), we created a “Ring” dataset from the Spitzer/GLIMPSE 8 μm and Spitzer/MIPSGAL 24 μm data and optimized the parameters of the CNN model. We applied the developed model to a range of 16.5° ≤ l ≤ 19.5°, |b| ≤ 1° . As a result, 234 “Ring” candidates are detected. The “Ring” candidates were matched with 75% Milky Way Project (MWP, Simpson et al. 2012) “Ring” and 65% WISE Hii region catalog (Anderson et al. 2014). In addition, new“Ring”and Hii region candidate objects were also found. For these results, we conclude that the CNN model may have a recognition accuracy equal to or better than that of human eyes.

Citation Download Citation

Shota Ueda, Shinji Fujita, Atsushi Nishimura, Toshikazu Onishi, Yoshito Shimajiri, Yusuke Miyamoto, Kazufumi Torii, Atsushi M. Ito, Shunya Takekawa, Hiroyuki Kaneko, Daisuke Yoshida, Taro Matsuo, Tsuyoshi Inoue, Yasutomo Kawanishi, and Kazuki Tokuda "Identification of infrared-ring structures by convolutional neural network", Proc. SPIE 11452, Software and Cyberinfrastructure for Astronomy VI, 114522L (13 December 2020); https://doi.org/10.1117/12.2560830

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available