Mr. Motoki Oeda Profile

Motoki Oeda

at Institute of Science Tokyo

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Publications (1)

Proceedings Article | 15 December 2020 Presentation

Development of transient detection method and GPU-accelerated image reduction pipeline

Katsuhiro Murata, Kota Iida, Masafumi Niwano, Yoichi Yatsu, Nobuyuki Kawai, Motoki Oeda, Kazuki Shiraishi, Ryo Adachi, Futa Ogawa, Sayaka Toma, Ryohei Hosokawa

Proceedings Volume 11449, 114491Z (2020) https://doi.org/10.1117/12.2561258

KEYWORDS: Detector development, Optical telescopes, Image analysis, X-ray telescopes, Astronomical imaging, Astronomy, Convolutional neural networks, Telescopes, Galactic astronomy, Library classification systems

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Identification of optical counterparts of gravitational waves (GWs) is one of the most exciting topics in astronomy. Since a typical sky map error region of the LIGO/Virgo is much larger than the field-of-view of optical telescopes, it is important to search and rapidly identify optical counterparts through follow-up observation by optical telescopes. The method of rapid and accurate transient detection in huge set of observed images is important. Motivated by this, we are developing transient detection method with convolutional neural network (CNN). We constructed CNN-based classifier designed to separate a transient image, an image including a transient source, and non-transient image. The input data is a pair of an observed image and a reference image. Here we adopt an image taken by MITSuME 50 cm telescope as observed and Pan-STARRS image as reference. We trained it with more than 10,000 images of 77 background galaxies within 200 Mpc. The training data with artificially transient images is made by adding an artificial point source into an observed image with various positions and luminosities. We tested the performance of the classifier with test data and found that the classification accuracy is more than 90%. Furthermore, we are developing a high-speed image reduction pipeline with GPU (Graphics Processing Unit) for real-time analysis of observed images. To accelerate image reduction, the pipeline uses CuPy (a python library for numerical calculation on the GPU) and minimize fits I/O. We found that the reduction speed of the pipeline achieves 30 times faster than IRAF for 240 set of 1024 x 1024 pixel images. In this talk, we will introduce the current status of the development of the transient detection method and the GPU-accelerated image reduction pipeline. We will also introduce our plan of installation of them into the systems of MITSuME 50cm telescopes in Akeno and Okayama which have performed optical follow-up observations of gamma-ray bursts, gravitational waves and X-ray binaries.

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