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Astrophysics > Instrumentation and Methods for Astrophysics

arXiv:2404.01049 (astro-ph)
[Submitted on 1 Apr 2024]

Title:A Novel Sector-Based Algorithm for an Optimized Star-Galaxy Classification

Authors:Anumanchi Agastya Sai Ram Likhit, Divyansh Tripathi, Akshay Agarwal
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Abstract:This paper introduces 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.
Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG)
Cite as: arXiv:2404.01049 [astro-ph.IM]
  (or arXiv:2404.01049v1 [astro-ph.IM] for this version)
  https://doi.org/10.48550/arXiv.2404.01049
Journal reference: The Second Tiny Papers Track at ICLR 2024

Submission history

From: Agastya Sai Ram Likhit Anumanchi [view email]
[v1] Mon, 1 Apr 2024 11:08:53 UTC (3,334 KB)
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