Generating Kilonova Light Curves Using Autoencoder to Investigate the Properties of a Compact Binary Merging System Surojit Saha ^{1*}, Laurence Datrier^{2}, Michael Williams^{2}, Albert Kong^{1}, Ik Siong Heng^{2}, Martin Hendry^{2}, Daniel Williams^{2}, Nicola De Lillo^{2}, Fergus Hayes^{2}^{1}Institute of Astronomy, National Tsing Hua University, Hsinchu City, Taiwan^{2}Institute for Gravitational Research, School of Physics and Astronomy, University of Glasgow, Glasgow, UK* Presenter:Surojit Saha, email:surojitsaha@gapp.nthu.edu.tw The discovery of the optical counterpart, along with the gravitational waves from GW170817, of the first binary neutron star merger, opened up a new era for multi-messenger astrophysics. The optical counterpart, designated as a kilonova (KN), has immense potential to reveal the nature of compact binary merging systems. Ejecta properties from the merging system provide important information about the total binary mass, the mass ratio, system geometry, and the equation of state of the merging system. In this study, we are using the light curves from models that can describe the observed light curve from GW170817 and we generate the light curves based on different ejecta properties such as lanthanide fraction, ejecta velocity and ejecta mass. We have been successful in generating the light curves using our autoencoder code. The generated light curve is quite in agreement with the original light curves. This method, further, will be applied to learn the light curves directly from observations. This will, consequently, help to identify the dependency of these properties on the merger system. This current work is amalgamated with neural networks and KN data analysis. It is expected that the final obtained results will be insightful towards the investigation of KN light curves and compact binary merger systems.
Keywords: Kilonova, Autoencoder, Light curve |