Application of the grade selection of X-ray events using machine learning for a CubeSat mission

Abstract

X-ray observation covering a wide field of view with high sensitivity is essential in searching for an electromagnetic counterpart of gravitational wave events. A lobster-eye optics (LEO) and a large area CMOS sensor are effective instruments to achieve this goal. Furthermore, thanks to the light weight of LEO, it can be installed on a small platform such as a CubeSat. However, the real-time identification of X-ray events is challenging with restricted resources on space. Therefore, we trained a image recognition network utilizing one of the machine learning models of convolutional neural network (CNN). Then, we use this network to identify X-ray events in the image taken from a CMOS sensor. Moreover, we use a Sony single-board computer, Spresense, that provides ultra-low power consumption and supports machine learning libraries for the process. This paper introduces our machine learning-based X-ray event selection process that is targeted for use on a CubeSat.