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'''Data Driven Astronomy(DDA)''' refers to the use of [[Data science|Data Science]] in [[Astronomy]]. Several outputs of [[Telescopic observational astronomy|telescopic observations]] and [[Astronomical survey|sky surveys]] are taken into consideration and approaches related to [[data mining]] and big data management are used to analyze, filter, and [[Normalization (statistics)|normalize]] the [[Data set|datasets]] that are further used for making Classifications, Predictions, and Anomaly detections by [[Advances in Statistics|advanced Statistical approaches]], [[Digital image processing|Digital Image Processing]] and [[Machine learning|Machine Learning]]. The output of these processes is used by [[Astronomer|Astronomers]] and Space Scientists to study and identify patterns, anomalies, and movements in outer space and conclude theories and discoveries in the [[cosmos]].▼
▲'''Data Driven Astronomy(DDA)''' refers to the use of [[Data science|Data Science]] in [[Astronomy]]. Several outputs of [[Telescopic observational astronomy|telescopic observations]] and [[Astronomical survey|sky surveys]] are taken into consideration and approaches related to [[data mining]] and big data management are used to analyze, filter, and [[Normalization (statistics)|normalize]] the [[Data set|datasets]] that are further used for making Classifications, Predictions, and Anomaly detections by [[Advances in Statistics|advanced Statistical approaches]], [[Digital image processing|Digital Image Processing]] and [[Machine learning|Machine Learning]]. The output of these processes is used by [[Astronomer
== History ==
In 2007, the [[Galaxy Zoo|Galaxy Zoo project]]<ref>{{Cite web |title=Zooniverse |url=https://www.zooniverse.org/projects/zookeeper/galaxy-zoo |access-date=2024-05-10 |website=www.zooniverse.org}}</ref> was launched for [[Galaxy morphological classification|morphological classification]]<ref>{{Cite journal |last=Cavanagh |first=Mitchell K. |last2=Bekki |first2=Kenji |last3=Groves |first3=Brent A. |date=2021-07-08 |title=Morphological classification of galaxies with deep learning: comparing 3-way and 4-way CNNs |url=http://arxiv.org/abs/2106.01571 |journal=Monthly Notices of the Royal Astronomical Society |volume=506 |issue=1 |pages=659–676 |doi=10.1093/mnras/stab1552 |issn=0035-8711}}</ref><ref>{{Cite journal |last=Goyal |first=Lalit Mohan |last2=Arora |first2=Maanak |last3=Pandey |first3=Tushar |last4=Mittal |first4=Mamta |date=2020-12-01 |title=Morphological classification of galaxies using Conv-nets |url=https://doi.org/10.1007/s12145-020-00526-w |journal=Earth Science Informatics |language=en |volume=13 |issue=4 |pages=1427–1436 |doi=10.1007/s12145-020-00526-w |issn=1865-0481}}</ref> of a large number of [[Galaxy|galaxies]]. In this project, 900,000 images were considered for classification that were taken from the [[Sloan Digital Sky Survey|Sloan Digital Sky Survey (SDSS)]]<ref name=":0">{{Cite web |title=Sloan Digital Sky Survey-V: Pioneering Panoptic Spectroscopy - SDSS-V |url=https://www.sdss.org/ |access-date=2024-05-10 |language=en-US}}</ref> for the past 7 years. The task was to study each picture of a galaxy, classify it as [[Elliptical galaxy|elliptical]] or [[Spiral galaxy|spiral]], and determine whether it was spinning or not. The team of Astrophysicists led by [[Kevin Schawinski]] in [[University of Oxford|Oxford University]] were in charge of this project and Kevin and his colleague [[Chris Lintott|Chris Linlott]] figured out that it would take a period of
== Methodology ==
The data retrieved from the sky surveys are first brought for [[Data preprocessing|Pre-processing]]. In this, [[Data redundancy|redundancies]] are removed and filtrated. Further, [[Feature engineering|feature extraction]] is performed on this filtered data set, which is further taken for processes.<ref name=":1">{{Cite journal |last=Zhang |first=Yanxia |last2=Zhao |first2=Yongheng |date=2015-05-22 |title=Astronomy in the Big Data Era |url=http://datascience.codata.org/article/10.5334/dsj-2015-011/ |journal=Data Science Journal |volume=14 |issue=0 |pages=11 |doi=10.5334/dsj-2015-011 |issn=1683-1470}}</ref>
* The Palomar Digital Sky Survey (DPOSS)<ref>{{Cite web |title=The Palomar Digital Sky Survey (DPOSS) |url=https://sites.astro.caltech.edu/~george/dposs/dposs_pop.html |access-date=2024-05-10 |website=sites.astro.caltech.edu}}</ref>
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* [[Square Kilometre Array|The Square Kilometer Array (SKA)]]<ref>{{Cite web |title=Explore {{!}} SKAO |url=https://www.skao.int/en |access-date=2024-05-10 |website=www.skao.int}}</ref>
The size of data from the above-mentioned sky surveys ranges from 3 [[Terabyte|TB]] to almost 4.6 [[Exabyte|EB]].<ref name=":1" />
=== Classification ===
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=== Regression ===
'''''Regression'''''<ref>{{Citation |last=Sarstedt |first=Marko |title=Regression Analysis |date=2014 |work=A Concise Guide to Market Research: The Process, Data, and Methods Using IBM SPSS Statistics |pages=193–233 |editor-last=Sarstedt |editor-first=Marko |url=https://doi.org/10.1007/978-3-642-53965-7_7 |access-date=2024-05-10 |place=Berlin, Heidelberg |publisher=Springer |language=en |doi=10.1007/978-3-642-53965-7_7 |isbn=978-3-642-53965-7 |last2=Mooi |first2=Erik |editor2-last=Mooi |editor2-first=Erik}}</ref> is used to make predictions based on the retrieved data through statistical trends and statistical modeling. Different uses of this technique are used for fetching [[Photometric redshift
* [[Artificial Neural Network|Artificial Neural Networks (ANN)]]
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* [[Decision tree|Decision Trees]]
== References
{{Reflist}}
{{Uncategorized|date=May 2024}}
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