Point source classification on astronomical photometric images using artificial neural network

Abstract

Gravitational-wave Optical Transient Observer (GOTO) is a set of telescopes primarily for optical follow-up observation of gravitational wave. It is designed to have as much sky coverage as 18 square-degree per pointing. This property allows the telescopes to compensate for an area of uncertainty produced by gravitational wave detector such as LIGO. The secondary objective of GOTO is to perform an all-sky survey. Each night, it could cover as much as 2700 square-degree. Such large area of observation produces as large number of objects. It was estimated to be able to collect 12 million objects per night. Processing such quantity of data manually is extremely difficult. One of the most challenging task is to perform object classification. A solution to this problem is to have machine perform classification, which is possible via machine learning algorithm. In this work, we use supervise machine learning performed on simulated data to create a point source classifier. We will focus on application and performance of artificial neural network in the work.