A micro-satellite for wide-field near-UV transient exploration PETREL is presented. The scientific goal is to detect transient sources, such as GW sources accompanied by EM emission, supernovae, and other unknown phenomena in the UV sky, and to reveal the nature of explosive phenomena in the universe through multimessenger observations. PETREL is equipped with an 80 mm refractor coupled with BI-CMOS detector. A powerful OBC automatically processes the received data and searches for orbital transients. If a transient is detected, an alert is immediately sent over the Globalstar network. A 50 kg class micro-satellite bus system is being developed for this mission. A series of functional tests using simulators have verified that the satellite system can detect transient sources in nearby galaxies as designed.
PETREL (Platform for Extra & Terrestrial Remote Examination with LCTF) is a 50kg class satellite with a 80mm-diameter Ultraviolet Telescope (UVT). The science missions of the UVT system are the discovery of high-energy transients, such as supernovae and gravitational-wave electromagnetic sources. To achieve them, the optical system is optimized to detect near-UV photons between 250 and 300 nm. Within a 30-minutes exposure time per every revolution, it surveys a remarkably wide field as 50 deg^2 with a high sensitivity of 20~ AB magnitude. The Board Unit has an on-board computer, which directly analyzes raw images obtained by a back-illuminated CMOS sensor. The computer can detect transients immediately, so as to alert the transient information to ground within 30 minutes of discovery. We will achieve the first UV survey to explore the transients in their early phase and reveal the underlying physical processes.
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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