Photometric identification of compact galaxies, stars and quasars using multiple neural networks
This work addresses the classification of compact objects in astronomy, which is incremental as it applies existing neural network architectures to a specific dataset.
The paper tackled the problem of identifying stars, quasars, and compact galaxies using photometric data from SDSS DR16, resulting in MargNet, a classifier that outperforms other methods for compact galaxies, even at fainter magnitudes.
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.