Statistical and Machine Learning Methods in Analyzing Light Curves of Kilonovae
Lately in Astronomy, lots of attention has been given to analyzing the variables in the equations modeling star and black hole collisions, especially with respect to their generated kilonovae. In order to have better data surrounding these events and understand the conditions under which these collisions occur, it is necessary to analyze the light curves of these kilonovae. Modern statistical and machine learning methods, such as Bayesian inference, as well as Principal Component Analysis and the use of Neural Networks, allow for proper understanding of these light curves with respect to different variables, and extracting the necessary information for their analysis. Skyportal is a new online platform designed to easily access astronomical data from a wide variety of sources all around the world, and is a valuable tool into which we wish to incorporate all of these features. This project intends to discuss design details and the potential inclusion of these features into the Skyportal platform, and provide useful documentation for their future implementation.