Uncovering Stellar Patterns and Anomalies Through Data-Driven Astronomy

Author(s)	Ujjal Adhikary
Country	India
Abstract	Abstract This paper explores data-driven methodologies in astronomy, leveraging large-scale datasets to investigate stellar properties, classification patterns, and potential anomalies in stellar evolution. Using machine learning and statistical tools, we analyze datasets from large sky surveys, including Gaia and SDSS, focusing on identifying unique features that traditional methods may overlook. Our findings demonstrate that data-driven approaches can uncover nuanced stellar behaviors and improve classification systems. Keywords: Data-driven astronomy, machine learning, stellar classification, anomaly detection, sky surveys.
Field	Physics > Astronomy
Published In	Volume 6, Issue 6, November-December 2024
Published On	2024-12-05
Cite This	Uncovering Stellar Patterns and Anomalies Through Data-Driven Astronomy - Ujjal Adhikary - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.31726
DOI	https://doi.org/10.36948/ijfmr.2024.v06i06.31726
Short DOI	https://doi.org/g8t3kv

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-5948-55	editor@ijfmr.com

International Journal For Multidisciplinary Research