Photometric identification of compact galaxies, stars, and quasars using multiple neural networks. Issue 2 (16th November 2022)

• Record Type: Journal Article
• Authors: Chaini, Siddharth; Bagul, Atharva; Deshpande, Anish; Gondkar, Rishi; Sharma, Kaushal; Vivek, M; Kembhavi, Ajit
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 518:Issue 2(2023)
• Page Start: 3123
• Page End: 3136
• Publication Date: 2022-11-16
• Subjects: methods: data analysis -- techniques: photometric -- software: data analysis -- stars: general -- galaxies: general -- quasars: general; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stac3336
• Languages: English
• ISSNs: 0035-8711

Abstract

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.