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2020天文年會
中研院天文所 國際會議廳

論文摘要

Generating Kilonova Light Curves Using Recurrent Neural Network/ Autoencoder to Investigate the Properties of a Compact Binary Merging System

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Surojit Saha(a)*, Albert Kong(a), Ik Siong Heng(b), Martin Hendry(b), Laurence Datrier(b), Michael Williams(b), Daniel Williams(b), Nicola De Lillo(b), Fergus Hayes(b)(a)Institute of Astronomy, National Tsing Hua University Hsinchu, R.O.C(b)Institute for Gravitational Research, School of Physics and Astronomy, University of Glasgow, Scotland

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, a neural network has been applied to learn the optical light curves of the KN associated with GW170817 using real data and we generate the light curves based on different ejecta properties such as lanthanide fraction, ejecta velocity and ejecta mass. Further, this method will be applied to learn the light curves directly from observations. This will further 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 obtained results will be insightful towards the investigation of KN light curves and compact binary merger systems. Keywords: Kilonova, neural network, light curve