Draft:Data Driven Astronomy
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Comment: The purpose of Wikipedia is not to host essays, and as such, this draft has been rejected. There are also no inline citations, an unclear citation style, and a plethora more of issues. thetechie@enwiki: ~/talk/ $ 16:07, 9 May 2024 (UTC)
- Comment: Please fix the issues before resubmitting. If you resubmit it again within a short timeframe without fixing anything, your draft will be rejected, meaning that you cannot resubmit it. thetechie@enwiki: ~/talk/ $ 02:29, 9 May 2024 (UTC)
Data-Driven Astronomy (DDA) is the contribution of Data science in the field of Astronomy, particularly through the emergence of Astronomical Data Science. These disciplines focus on using advanced statistical methods, Digital image processing, and Data mining to analyze vast datasets from astronomical observations. It plays a role in various aspects of Astronomy, including galaxy classification, anomaly detection, and feature extraction. In 2007, Galaxy Zoo project utilized DDA techniques to classify galaxies based on their shapes and spin, a task that would have been extremely time-consuming using traditional methods. The processes in DDA involve Data collection, Data analysis and Normalization which are used in Classifications and Predictions using Machine learning to cluster and classify Astronomical object like Galaxy, Meteoroid, Nebula and Star. The processes involve manipulation of Big data which is obtained from several repositories and outputs of Astronomical survey conducted by several laboratories and organizations. The heaviest data is released by the Pan-STARRS [1] comprising of 1.6 Petabytes of data. DDA impacts reducing the workloads of processing these datasets and enhances Astronomers and Space Scientists to focus on patterns and conclusions to work on the advancement of existing theories.
Methodology[edit]
One of the main challenges in Data-Driven Astronomy is the processing and analysis of large datasets, which can range from Terabytes to Exabytes in size. To address this challenge, astronomers use Parallel computing, Cloud computing, and advanced Data processing techniques to efficiently handle and analyze massive amounts of data.
Technical Requirements Involved[edit]
Data mining techniques, such as Cluster analysis and Anomaly detection, are widely used to analyze astronomical data. These methods help astronomers identify patterns, outliers, and rare objects in the universe, leading to discoveries and insights.
References[edit]
[1] S. Pati, "How Data Science is Used in Astronomy?", June 2021, https://www.analyticsinsight.net/how-data-science-is-used-in-astronomy/
[2] Y. Zhang, Y. Zhao, "Astronomy in the Big Data Era", CoData Data Science Journal
[4] Sloan Digital Sky Surveys, SDSS, https://www.sdss4.org/surveys/
[5] J. Peak, "Data Science in Astronomy, Three Ways", STScl Newsletter 35(1), 2018, https://www.stsci.edu/contents/newsletters/2018-volume-35-issue-01/data-science-in-astronomy-three-ways
[6] D. Bayron, "Machine Learning in Astronomy: A practical overview", arXiv:1904.07248 [astro-ph.IM], [1]