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 (SDSS) Data Release 16 (DR16) catalogue. MargNet consists of a combination of Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) architectures. Using a carefully curated dataset consisting of 240,000 compact objects and an additional 150,000 faint objects, the machine learns classification directly from the data, minimising 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 (DES) and images from the Vera C. Rubin Observatory.
@article{arxiv.2211.08388,
title = {Photometric identification of compact galaxies, stars and quasars using multiple neural networks},
author = {Siddharth Chaini and Atharva Bagul and Anish Deshpande and Rishi Gondkar and Kaushal Sharma and M. Vivek and Ajit Kembhavi},
journal= {arXiv preprint arXiv:2211.08388},
year = {2022}
}
Comments
14 pages, 10 figures, Accepted for publication in MNRAS