astroinformatics

Astroinformatics is primarily focused on developing the tools, methods, and applications of computational science, data science, machine learning, and statistics for research and education in data-oriented astronomy.{{cite journal |last1=Borne |first1=Kirk D. |title=Astroinformatics: data-oriented astronomy research and education |journal=Earth Science Informatics |date=12 May 2010 |volume=3 |issue=1–2 |pages=5–17 |doi=10.1007/s12145-010-0055-2|s2cid=207393013 }} Early efforts in this direction included data discovery, metadata standards development, data modeling, astronomical data dictionary development, data access, information retrieval,{{cite arXiv|last1=Borne|first1=Kirk|title=Science User Scenarios for a Virtual Observatory Design Reference Mission: Science Requirements for Data Mining|eprint=astro-ph/0008307|year=2000}} data integration, and data mining{{cite book |editor-last1=Kargupta |editor-first1=Hillol |display-editors=etal |title=Next generation of data mining |date=2008 |location=London |isbn=9781420085860 |last1=Borne |first1=Kirk|chapter=Scientific Data Mining in Astronomy |publisher=CRC Press |pages=91–114}} in the astronomical Virtual Observatory initiatives.{{cite book |doi=10.1117/12.487536|chapter=Distributed data mining in the National Virtual Observatory|title=Data Mining and Knowledge Discovery: Theory, Tools, and Technology V|volume=5098|pages=211–218|year=2003|last1=Borne|first1=Kirk D|s2cid=28195520|editor1-first=Belur V|editor1-last=Dasarathy}}{{cite journal |last1=Laurino |first1=O. |last2=D’Abrusco |first2=R. |last3=Longo |first3=G. |last4=Riccio |first4=G. |title=Astroinformatics of galaxies and quasars: a new general method for photometric redshifts estimation |journal=Monthly Notices of the Royal Astronomical Society |date=21 December 2011 |volume=418 |issue=4 |pages=2165–2195 |doi=10.1111/j.1365-2966.2011.19416.x|doi-access=free |arxiv=1107.3160 |bibcode=2011MNRAS.418.2165L |s2cid=7115554 }} Further development of the field, along with astronomy community endorsement, was presented to the National Research Council (United States) in 2009 in the astroinformatics "state of the profession" position paper for the 2010 Astronomy and Astrophysics Decadal Survey.{{cite journal|last1=Borne|first1=Kirk|title=Astroinformatics: A 21st Century Approach to Astronomy|journal=Astro2010: The Astronomy and Astrophysics Decadal Survey|arxiv = 0909.3892 |year=2009|volume=2010|pages=P6|bibcode=2009astro2010P...6B}} That position paper provided the basis for the subsequent more detailed exposition of the field in the Informatics Journal paper Astroinformatics: Data-Oriented Astronomy Research and Education.

Astroinformatics as a distinct field of research was inspired by work in the fields of Geoinformatics, Cheminformatics, Bioinformatics, and through the eScience work{{cite web|title=Online Science|url=http://research.microsoft.com/en-us/um/people/gray/JimGrayTalks.htm|website=Talks by Jim Gray|publisher=Microsoft Research|accessdate=11 January 2015}} of Jim Gray (computer scientist) at Microsoft Research, whose legacy was remembered and continued through the Jim Gray eScience Awards.{{cite web|title=Jim Gray eScience Award|url=http://research.microsoft.com/en-us/collaboration/focus/escience/jim-gray-award.aspx|website=Microsoft Research}}

Although the primary focus of astroinformatics is on the large worldwide distributed collection of digital astronomical databases, image archives, and research tools, the field recognizes the importance of legacy data sets as well—using modern technologies to preserve and analyze historical astronomical observations. Some Astroinformatics practitioners help to digitize historical and recent astronomical observations and images in a large database for efficient retrieval through web-based interfaces.[http://www.casca.ca/lrp2010/Docs/LRPReports/astroinformatics_lrp.pdf Astroinformatics in Canada], Nicholas M. Ball, David Schade. Retrieved 1 November 2012. Another aim is to help develop new methods and software for astronomers, as well as to help facilitate the process and analysis of the rapidly growing amount of data in the field of astronomy.{{cite web|title='Astroinformatics' helps Astronomers explore the sky|url=http://phys.org/news/2013-10-astroinformatics-astronomers-exploring-sky.html|website=Phys.org|publisher=Heidelberg University|accessdate=11 January 2015}}

Astroinformatics is described as the "fourth paradigm" of astronomical research.{{cite web|title=The Fourth Paradigm: Data-Intensive Scientific Discovery|url=https://www.microsoft.com/en-us/research/publication/fourth-paradigm-data-intensive-scientific-discovery/|website=Microsoft Research|date=October 2009|last1=Hey|first1=Tony}} There are many research areas involved with astroinformatics, such as data mining, machine learning, statistics, visualization, scientific data management, and semantic science.{{cite book|doi=10.1007/978-94-007-5618-2_9|chapter=Virtual Observatories, Data Mining, and Astroinformatics|title=Planets, Stars and Stellar Systems|pages=403–443|year=2013|last1=Borne|first1=Kirk|isbn=978-94-007-5617-5}} Data mining and machine learning play significant roles in astroinformatics as a scientific research discipline due to their focus on "knowledge discovery from data" (KDD) and "learning from data".{{Cite journal |last1=Ball |first1=N.M. |last2=Brunner |first2=R.J. |title=Data Mining and Machine Learning in Astronomy |journal=International Journal of Modern Physics D |volume=19 |issue=7 |pages=1049–1106 |doi=10.1142/S0218271810017160 |year=2010|arxiv=0906.2173 |bibcode=2010IJMPD..19.1049B |s2cid=119277652 }}{{cite book |doi=10.1063/1.3059074 |chapter=The LSST Data Mining Research Agenda |title=AIP Conference Proceedings |pages=347–351 |year=2008 |last1=Borne |first1=K |last2=Becla |first2=J |last3=Davidson |first3=I |last4=Szalay |first4=A |last5=Tyson |first5=J. A |last6=Bailer-Jones |first6=Coryn A.L|arxiv=0811.0167 |s2cid=118399971 }}

The amount of data collected from astronomical sky surveys has grown from gigabytes to terabytes throughout the past decade and is predicted to grow in the next decade into hundreds of petabytes with the Large Synoptic Survey Telescope and into the exabytes with the Square Kilometre Array.{{cite book|doi=10.1063/1.3059076|chapter=Parametrization and Classification of 20 Billion LSST Objects: Lessons from SDSS|title=AIP Conference Proceedings|pages=359–365|year=2008|last1=Ivezić|first1=Ž|last2=Axelrod|first2=T|last3=Becker|first3=A. C|last4=Becla|first4=J|last5=Borne|first5=K|last6=Burke|first6=D. L|last7=Claver|first7=C. F|last8=Cook|first8=K. H|last9=Connolly|first9=A|last10=Gilmore|first10=D. K|last11=Jones|first11=R. L|last12=Jurić|first12=M|last13=Kahn|first13=S. M|last14=Lim|first14=K.-T|last15=Lupton|first15=R. H|last16=Monet|first16=D. G|last17=Pinto|first17=P. A|last18=Sesar|first18=B|last19=Stubbs|first19=C. W|last20=Tyson|first20=J. A|last21=Bailer-Jones|first21=Coryn A.L|journal=AIP Conf. Proc.|volume=1082|arxiv=0810.5155|s2cid=117914490}} This plethora of new data both enables and challenges effective astronomical research. Therefore, new approaches are required. In part due to this, data-driven science is becoming a recognized academic discipline. Consequently, astronomy (and other scientific disciplines) are developing information-intensive and data-intensive sub-disciplines to an extent that these sub-disciplines are now becoming (or have already become) standalone research disciplines and full-fledged academic programs. While many institutes of education do not boast an astroinformatics program, such programs most likely will be developed in the near future.

Informatics has been recently defined as "the use of digital data, information, and related services for research and knowledge generation". However the usual, or commonly used definition is "informatics is the discipline of organizing, accessing, integrating, and mining data from multiple sources for discovery and decision support." Therefore, the discipline of astroinformatics includes many naturally-related specialties including data modeling, data organization, etc. It may also include transformation and normalization methods for data integration and information visualization, as well as knowledge extraction, indexing techniques, information retrieval and data mining methods. Classification schemes (e.g., taxonomies, ontologies, folksonomies, and/or collaborative tagging{{cite web|last1=Borne|first1=Kirk|title=Collaborative Annotation for Scientific Data Discovery and Reuse|url=http://www.asis.org/Bulletin/Apr-13/AprMay13_RDAP_Borne.html|website=Bulletin of the ASIS&T|publisher=American Society for Information Science and Technology|accessdate=11 January 2016|archive-url=https://web.archive.org/web/20160305073440/http://www.asis.org/Bulletin/Apr-13/AprMay13_RDAP_Borne.html|archive-date=5 March 2016|url-status=dead}}) plus Astrostatistics will also be heavily involved. Citizen science projects (such as Galaxy Zoo) also contribute highly valued novelty discovery, feature meta-tagging, and object characterization within large astronomy data sets. All of these specialties enable scientific discovery across varied massive data collections, collaborative research, and data re-use, in both research and learning environments.

In 2007, the Galaxy Zoo project{{Cite web |title=Zooniverse |url=https://www.zooniverse.org/projects/zookeeper/galaxy-zoo |access-date=2024-05-10 |website=www.zooniverse.org}} was launched for morphological classification{{Cite journal |last1=Cavanagh |first1=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 |journal=Monthly Notices of the Royal Astronomical Society |volume=506 |issue=1 |pages=659–676 |doi=10.1093/mnras/stab1552 |doi-access=free |arxiv=2106.01571 |issn=0035-8711}}{{Cite journal |last1=Goyal |first1=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|url-access=subscription }} of a large number of galaxies. In this project, 900,000 images were considered for classification that were taken from the Sloan Digital Sky Survey (SDSS){{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}} for the past 7 years. The task was to study each picture of a galaxy, classify it as elliptical or spiral, and determine whether it was spinning or not. The team of Astrophysicists led by Kevin Schawinski in Oxford University were in charge of this project and Kevin and his colleague Chris Linlott figured out that it would take a period of 3–5 years for such a team to complete the work.{{Cite web |last=Pati |first=Satavisa |date=2021-06-18 |title=How Data Science is Used in Astronomy? |url=https://www.analyticsinsight.net/data-science/how-data-science-is-used-in-astronomy |access-date=2024-05-10 |website=Analytics Insight |language=en}} There they came up with the idea of using Machine Learning and Data Science techniques for analyzing the images and classifying them.{{Citation |last=Baron |first=Dalya |title=Machine Learning in Astronomy: a practical overview |date=2019-04-15 |arxiv=1904.07248}}

In 2012, two position papers{{cite web|last1=Borne|first1=Kirk|title=Astroinformatics in a Nutshell|url=https://asaip.psu.edu/Articles/astroinformatics-in-a-nutshell|website=asaip.psu.edu|publisher=The Astrostatistics and Astroinformatics Portal, Penn State University|accessdate=11 January 2016}}{{cite web|last1=Feigelson|first1=Eric|title=Astrostatistics in a Nutshell|url=https://asaip.psu.edu/Articles/astrostatistics-in-a-nutshell|website=asaip.psu.edu|publisher=The Astrostatistics and Astroinformatics Portal, Penn State University|accessdate=11 January 2016}} were presented to the Council of the American Astronomical Society that led to the establishment of formal working groups in astroinformatics and Astrostatistics for the profession of astronomy within the US and elsewhere.{{cite journal|last1=Feigelson|first1=E.|last2=Ivezić|first2=Ž.|last3=Hilbe|first3=J.|last4=Borne|first4=K.|title=New Organizations to Support Astroinformatics and Astrostatistics|journal=Astronomical Data Analysis Software and Systems Xxii|arxiv=1301.3069|year=2013|volume=475|pages=15|bibcode=2013ASPC..475...15F}}

Astroinformatics provides a natural context for the integration of education and research.{{cite journal|last1=Borne|first1=Kirk|title=The Revolution in Astronomy Education: Data Science for the Masses|journal=Astro2010: The Astronomy and Astrophysics Decadal Survey|arxiv = 0909.3895 |year=2009|volume=2010|pages=P7|bibcode=2009astro2010P...7B}} The experience of research can now be implemented within the classroom to establish and grow data literacy through the easy re-use of data.{{cite web|title=Using Data in the Classroom|url=http://serc.carleton.edu/usingdata/index.html|website=Science Education Resource Center at Carleton College|publisher=National Science Digital Library|accessdate=11 January 2016}} It also has many other uses, such as repurposing archival data for new projects, literature-data links, intelligent retrieval of information, and many others.{{cite book|last1=Borne|first1=Kirk|title=Astroinformatics: Data-Oriented Astronomy|location=George Mason University, USA|url=http://www.iccs-meeting.org/iccs2009/PosterPapers/Poster-paper18.pdf|accessdate=January 21, 2015}}

The data retrieved from the sky surveys are first brought for data preprocessing. In this, redundancies are removed and filtrated. Further, feature extraction is performed on this filtered data set, which is further taken for processes.{{Cite journal |last1=Zhang |first1=Yanxia |last2=Zhao |first2=Yongheng |date=2015-05-22 |title=Astronomy in the Big Data Era |journal=Data Science Journal |volume=14 |pages=11 |doi=10.5334/dsj-2015-011 |doi-access=free |bibcode=2015DatSJ..14...11Z |issn=1683-1470}} Some of the renowned sky surveys are listed below:

• The Palomar Digital Sky Survey (DPOSS){{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}}
• The Two-Micron All Sky Survey (2MASS){{Cite web |title=IRSA - Two Micron All Sky Survey (2MASS) |url=https://irsa.ipac.caltech.edu/Missions/2mass.html |access-date=2024-05-10 |website=irsa.ipac.caltech.edu}}
• Green Bank Telescope (GBT){{Cite web |date=2023-06-26 |title=GBT |url=https://greenbankobservatory.org/portal/gbt/ |access-date=2024-05-10 |website=Green Bank Observatory |language=en-US}}
• The Galaxy Evolution Explorer (GALEX){{Cite web |title=GALEX - Galaxy Evolution Explorer |url=http://www.galex.caltech.edu/ |access-date=2024-05-10 |website=www.galex.caltech.edu}}
• The Sloan Digital Sky Survey (SDSS)
• SkyMapper Southern Sky Survey (SMSS){{Cite web |title=SkyMapper Southern Sky Survey |url=https://skymapper.anu.edu.au/ |access-date=2024-05-10 |website=skymapper.anu.edu.au}}
• The Panoramic Survey Telescope and Rapid Response System (PanSTARRS){{Cite web |title=Pan-STARRS1 data archive home page - PS1 Public Archive - STScI Outerspace |url=https://outerspace.stsci.edu/display/PANSTARRS/ |access-date=2024-05-10 |website=outerspace.stsci.edu}}
• The Large Synoptic Survey Telescope (LSST){{Cite web |last=Telescope |first=Large Synoptic Survey |title=Rubin Observatory |url=https://www.lsst.org/ |access-date=2024-05-10 |website=Rubin Observatory |language=en}}
• The Square Kilometer Array (SKA){{Cite web |title=Explore {{!}} SKAO |url=https://www.skao.int/en |access-date=2024-05-10 |website=www.skao.int}}

The size of data from the above-mentioned sky surveys ranges from 3 TB to almost 4.6 EB. Further, data mining tasks that are involved in the management and manipulation of the data involve methods like classification, regression, clustering, anomaly detection, and time-series analysis. Several approaches and applications for each of these methods are involved in the task accomplishments.

Classification{{Cite book |last1=Chowdhury |first1=Shovan |last2=Schoen |first2=Marco P. |chapter=Research Paper Classification using Supervised Machine Learning Techniques |date=2020-10-02 |title=2020 Intermountain Engineering, Technology and Computing (IETC) |chapter-url=https://ieeexplore.ieee.org/document/9249211 |publisher=IEEE |pages=1–6 |doi=10.1109/IETC47856.2020.9249211 |isbn=978-1-7281-4291-3}} is used for specific identifications and categorizations of astronomical data such as Spectral classification, Photometric classification, Morphological classification, and classification of solar activity. The approaches of classification techniques are listed below:

• Artificial neural network (ANN)
• Support vector machine (SVM)
• Learning vector quantization (LVQ)
• Decision tree
• Random forest
• k-nearest neighbors
• Naïve Bayesian networks
• Radial basis function network
• Gaussian process
• Decision table
• Alternating decision tree (ADTree)

Regression{{Citation |last1=Sarstedt |first1=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|url-access=subscription }} 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 redshifts and measurements of physical parameters of stars.{{Cite journal |title=Bulletin de la Société Royale des Sciences de Liège {{!}} PoPuPS |url=https://popups.uliege.be/0037-9565/index.php |journal=Bulletin de la Société Royale des Sciences de Liège |language=fr |issn=0037-9565}} The approaches are listed below:

• Artificial neural network (ANN)
• Support vector regression (SVR)
• Decision tree
• Random forest
• k-nearest neighbors regression
• Kernel regression
• Principal component regression (PCR)
• Gaussian process
• Least squared regression (LSR)
• Partial least squares regression

Clustering{{Cite book |last1=Bindra |first1=Kamalpreet |last2=Mishra |first2=Anuranjan |chapter=A detailed study of clustering algorithms |date=September 2017 |title=2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) |chapter-url=https://ieeexplore.ieee.org/document/8342454 |publisher=IEEE |pages=371–376 |doi=10.1109/ICRITO.2017.8342454 |isbn=978-1-5090-3012-5}} is classifying objects based on a similarity measure metric. It is used in Astronomy for Classification as well as Special/rare object detection. The approaches are listed below:

• Principal component analysis (PCA)
• DBSCAN
• k-means clustering
• OPTICS
• Cobweb model
• Self-organizing map (SOM)
• Expectation Maximization
• Hierarchical Clustering
• AutoClass{{Cite journal |last1=Pizzuti |first1=C. |last2=Talia |first2=D. |date=May 2003 |title=P-autoclass: scalable parallel clustering for mining large data sets |url=https://ieeexplore.ieee.org/document/1198395 |journal=IEEE Transactions on Knowledge and Data Engineering |language=en |volume=15 |issue=3 |pages=629–641 |doi=10.1109/TKDE.2003.1198395 |issn=1041-4347|url-access=subscription }}
• Gaussian Mixture Modeling (GMM)

Anomaly detection{{Cite journal |last1=Thudumu |first1=Srikanth |last2=Branch |first2=Philip |last3=Jin |first3=Jiong |last4=Singh |first4=Jugdutt (Jack) |date=2020-07-02 |title=A comprehensive survey of anomaly detection techniques for high dimensional big data |journal=Journal of Big Data |volume=7 |issue=1 |pages=42 |doi=10.1186/s40537-020-00320-x |doi-access=free |issn=2196-1115|hdl=10536/DRO/DU:30158643 |hdl-access=free }} is used for detecting irregularities in the dataset. However, this technique is used here to detect rare/special objects. The following approaches are used:

• Principal Component Analysis (PCA)
• k-means clustering
• Expectation Maximization
• Hierarchical clustering
• One-class SVM

Time-Series analysis{{Cite book |url=https://onlinelibrary.wiley.com/doi/book/10.1002/0471264385 |title=Handbook of Psychology |date=2003-04-15 |publisher=Wiley |isbn=978-0-471-17669-5 |editor-last=Weiner |editor-first=Irving B. |edition=1 |language=en |doi=10.1002/0471264385.wei0223}} helps in analyzing trends and predicting outputs over time. It is used for trend prediction and novel detection (detection of unknown data). The approaches used here are:

• Artificial neural network (ANN)
• Support vector regression (SVR)
• Decision tree

Additional conferences and conference lists:

• Astronomy and Computing
• Astrophysics Data System
• Astrophysics Source Code Library
• Astrostatistics
• Committee on Data for Science and Technology
• Data-driven astronomy
• Galaxy Zoo
• International Astrostatistics Association
• International Virtual Observatory Alliance (IVOA)
• MilkyWay@home
• Virtual Observatory
• WorldWide Telescope
• Zooniverse

{{reflist}}

• [http://astroinformatics.info/ International AstroInformatics Association] (IAIA)
• [http://www.adass.org/ Astronomical Data Analysis Software and Systems] (ADASS)
• [https://asaip.psu.edu/ Astrostatistics and Astroinformatics Portal]
• [https://cosmostatistics-initiative.org/ Cosmostatistics Initiative] (COIN)
• [http://www.iau.org/science/scientific_bodies/commissions/B3/ Astroinformatics and Astrostatistics Commission of the International Astronomical Union]

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