SG Task: Try to figure out which image corresponds to a star and which corresponds to a galaxy. (Answers: (a) - Star, (b) - Galaxy, (c) - Star, (d) - Galaxy, (e) - Galaxy )

The above-demonstrated task consists of real observation data from SDSS DR18, and we can see that in many cases, it is impossible for humans with the naked eye to classify a source as a star or galaxy.

We devloped a novel sector-based methodology for star-galaxy classification, leveraging the latest Sloan Digital Sky Survey data (SDSS-DR18). By strategically segmenting the sky into sectors aligned with SDSS observational patterns and employing a dedicated convolutional neural network (CNN), we achieve state-of-the-art performance for star galaxy classification. Our preliminary results demonstrate a promising pathway for efficient and precise astronomical analysis, especially in real-time observational settings.

Recently, several research works have been developed to help astronomers by automatically classifying the galaxies (Soumagnac et al., 2015; Ba Alawi & Al-Roainy, 2021; Chaini et al., 2022; Kim & Brunner, 2016; Garg et al., 2022). However, these models perform well but are complex. In contrast to the existing work, due to the complexity of our star-galaxy system, in this research, we have proposed the development of a classification approach utilizing a sector-based division of the sky.

We have proposed a novel and cost-effective algorithm for star-galaxy classification by handling sector-specific data. The efficacy of the proposed algorithm surpasses the existing algorithm back our idea of segregating the sky into sectors for better performance. In the future, we aim to develop an advanced architecture to tackle other sectors and improve the classification performance of the proposed approach by incorporating sector-specific auxiliary information. We believe the proposed research can advance the astronomical research by precisely identifying the celestial objects.

Publications

We submitted this work to the ICLR Conference 2024 as a tiny paper, had it peer reviewed by three experts in the field, and received acceptance as a paper. The details are as follows:

@inproceedings{ likhit2024a, title={A Novel Sector-Based Algorithm for an Optimized Star-Galaxy Classification}, author={Anumanchi Agastya Sai Ram Likhit and Divyansh Tripathi and Akshay Agarwal}, booktitle={The Second Tiny Papers Track at ICLR 2024}, year={2024}, url={https://openreview.net/forum?id=HzEefCle2c} }

Anumanchi Agastya Sai Ram Likhit