A Novel Sector-Based Algorithm for an Optimized Star-Galaxy Classification
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916293775458304 |
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| author | Likhit, Anumanchi Agastya Sai Ram Tripathi, Divyansh Agarwal, Akshay |
| author_facet | Likhit, Anumanchi Agastya Sai Ram Tripathi, Divyansh Agarwal, Akshay |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_01049 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Novel Sector-Based Algorithm for an Optimized Star-Galaxy Classification Likhit, Anumanchi Agastya Sai Ram Tripathi, Divyansh Agarwal, Akshay Instrumentation and Methods for Astrophysics Machine Learning 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. |
| title | A Novel Sector-Based Algorithm for an Optimized Star-Galaxy Classification |
| topic | Instrumentation and Methods for Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2404.01049 |