Automatic detection of microlensing events in the galactic bulge using machine learning techniques

Abstract

The Wide Field Infrared Survey Telescope (WFIRST) is a NASA flagship mission scheduled to launch in mid-2020, with more than one year of its lifetime dedicated to microlensing survey. The survey is to discover thousands of exoplanets near or beyond the snowline via their microlensing light curve signatures, enabling a Kepler-like statistical analysis of planets at ~1-10 AU from their host stars and potentially revolutionizing our understanding of planet formation. The goal of our work is to create an automated system that has the ability to efficiently process and classify large-scale astronomical datasets that missions such as WFIRST will produce. In this paper, we discuss our framework that utilizes features election and parameter optimization for classification models to automatically differentiate the different types of stellar variability and detect microlensing events.