Photometric identification of compact galaxies, stars and quasars using multiple neural networks

TL;DR

This paper introduces MargNet, a deep learning classifier combining CNN and ANN architectures, for accurately identifying stars, quasars, and compact galaxies from SDSS data, especially at faint magnitudes.

Contribution

The paper presents MargNet, a novel deep learning model specifically designed for classifying compact galaxies, outperforming previous methods and applicable to upcoming large-scale surveys.

Findings

MargNet achieves higher classification accuracy than existing methods.

The model performs well on faint objects in SDSS data.

Feature engineering enhances deep learning classification performance.

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 (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.