A machine learning technique to classify LSST observed astronomical objects based on photometric data

Abstract

The light curve analysis of the heavenly bodies is an indispensable tool for understanding the physical phenomena that govern them. Large telescopes like the LSST will produce an excess of data produced that will necessitate the need for automated methods to sift through it quickly and efficiently as doing so manually can be truly laborious. Furthermore, such a method should be able classify the observed astronomical objects accurately. Keeping this in view, we have proposed an automated classification method using the simulated, photometric light curves in to 14 different classes. We have built our classification model by extracting several features and employing Random Forest classifier. Our proposed methodology performs reasonably well for most of the classes while others still offer a little room for improvement. As our proposed methodology relies on features extracted from photometric light curves, therefore it can be adapted and extended for use in other fields that rely on similar light curves.

Keywords Feature extraction, Telescopes, Passband, Planets, Shape