Abstract:
We present MargNet, a deep learning-based classifier for identifying stars, quasars, and compact galaxies using photometric
parameters and images from the Sloan Digital Sky Survey Data Release 16 catalogue. MargNet consists of a combination of
convolutional neural network and artificial neural network architectures. Using a carefully curated data set consisting of 240 000
compact objects and an additional 150 000 faint objects, the machine learns classification directly from the data, minimizing
the need for human intervention. MargNet is the first classifier focusing exclusively on compact galaxies and performs better
than other methods to classify compact galaxies from stars and quasars, even at fainter magnitudes. This model and feature
engineering in such deep learning architectures will provide greater success in identifying objects in the ongoing and upcoming
surveys, such as Dark Energy Survey and images from the Vera C. Rubin Observatory.
