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Astroinformatics

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Astroinformatics is an interdisciplinary field of study involving the combination of astronomy, data science, informatics, and information/communications technologies.[1][2]

Background

Astroinformatics is primarily focused on developing the tools, methods, and applications of computational science, data science, and statistics for research and education in data-oriented astronomy.[1] Early efforts in this direction included data discovery, metadata standards development, data modeling, astronomical data dictionary development, data access, information retrieval,[3] data integration, and data mining[4] in the astronomical Virtual Observatory initiatives.[5][6][7] 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.[8] 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.[1]

Astroinformatics as a distinct field of research was inspired by work in the fields of Bioinformatics and Geoinformatics, and through the eScience work[9] of Jim Gray (computer scientist) at Microsoft Research, whose legacy was remembered and continued through the Jim Gray eScience Awards.[10]

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.[2][11] 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.[12]

Astroinformatics is described as the Fourth Paradigm of astronomical research.[13] There are many research areas involved with astroinformatics, such as data mining, machine learning, statistics, visualization, scientific data management, and semantic science.[6] 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".[14][15]

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.[16] 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[17]) 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 2012, two position papers[18][19] 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.[20]

Astroinformatics provides a natural context for the integration of education and research.[21] The experience of research can now be implemented within the classroom to establish and grow data literacy through the easy re-use of data.[22] It also has many other uses, such as repurposing archival data for new projects, literature-data links, intelligent retrieval of information, and many others.[23]

Conferences

Year Place Link
2018 Heidelberg, Germany [1]
2017 Cape Town, South Africa [2]
2016 Sorrento, Italy [3]
2015 Dubrovnik, Dalmatia [4]
2014 University of Chile [5]
2013 Australia Telescope National Facility, CSIRO [6]
2012 Microsoft Research [7]
2011 Sorrento, Italy [8]
2010 Caltech [9]

See also

References

  1. ^ a b c Borne, Kirk D. (12 May 2010). "Astroinformatics: data-oriented astronomy research and education". Earth Science Informatics. 3 (1–2): 5–17. doi:10.1007/s12145-010-0055-2.
  2. ^ a b Astroinformatics and digitization of astronomical heritage, Nikolay Kirov. The fifth SEEDI International Conference Digitization of cultural and scientific heritage, May 19–20, 2010, Sarajevo. Retrieved 1 November 2012.
  3. ^ Borne, Kirk (2000). "Science User Scenarios for a Virtual Observatory Design Reference Mission: Science Requirements for Data Mining". arXiv:astro-ph/0008307.
  4. ^ Borne, Kirk (2008). "Scientific Data Mining in Astronomy". In Kargupta, Hillol; et al. (eds.). Next generation of data mining. London: CRC Press. pp. 91–114. ISBN 9781420085860.
  5. ^ Borne, Kirk D (2003). "Distributed data mining in the National Virtual Observatory". Data Mining and Knowledge Discovery: Theory, Tools, and Technology V. Data Mining and Knowledge Discovery: Theory, Tools, and Technology V. Vol. 5098. p. 211. doi:10.1117/12.487536.
  6. ^ a b Borne, Kirk (2013). "Virtual Observatories, Data Mining, and Astroinformatics". Planets, Stars and Stellar Systems. p. 403. doi:10.1007/978-94-007-5618-2_9. ISBN 978-94-007-5617-5.
  7. ^ Laurino, O.; D’Abrusco, R.; Longo, G.; Riccio, G. (21 December 2011). "Astroinformatics of galaxies and quasars: a new general method for photometric redshifts estimation". Monthly Notices of the Royal Astronomical Society. 418 (4): 2165–2195. arXiv:1107.3160. Bibcode:2011MNRAS.418.2165L. doi:10.1111/j.1365-2966.2011.19416.x.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  8. ^ Borne, Kirk (2009). "Astroinformatics: A 21st Century Approach to Astronomy". arXiv:0909.3892 [astro-ph.IM].
  9. ^ "'Online Science'". Talks by Jim Gray. Microsoft Research. Retrieved 11 January 2015.
  10. ^ "Jim Gray eScience Award". Microsoft Research.
  11. ^ Astroinformatics in Canada, Nicholas M. Ball, David Schade. Retrieved 1 November 2012.
  12. ^ "'Astroinformatics' helps Astronomers explore the sky". Phys.org. Heidelberg University. Retrieved 11 January 2015.
  13. ^ "The Fourth Paradigm: Data-Intensive Scientific Discovery". Microsoft Research.
  14. ^ Ball, N.M.; Brunner, R.J. (2010). "Data Mining and Machine Learning in Astronomy". International Journal of Modern Physics D. 19 (7): 1049. arXiv:0906.2173. Bibcode:2010IJMPD..19.1049B. doi:10.1142/S0218271810017160.
  15. ^ Borne, K; Becla, J; Davidson, I; Szalay, A; Tyson, J. A; Bailer-Jones, Coryn A.L (2008). "The LSST Data Mining Research Agenda". AIP Conference Proceedings. p. 347. arXiv:0811.0167. doi:10.1063/1.3059074.
  16. ^ Ivezić, Ž; Axelrod, T; Becker, A. C; Becla, J; Borne, K; Burke, D. L; Claver, C. F; Cook, K. H; Connolly, A; Gilmore, D. K; Jones, R. L; Jurić, M; Kahn, S. M; Lim, K.-T; Lupton, R. H; Monet, D. G; Pinto, P. A; Sesar, B; Stubbs, C. W; Tyson, J. A; Bailer-Jones, Coryn A.L (2008). "Parametrization and Classification of 20 Billion LSST Objects: Lessons from SDSS". AIP Conference Proceedings. p. 359. arXiv:0810.5155. doi:10.1063/1.3059076.
  17. ^ Borne, Kirk. "Collaborative Annotation for Scientific Data Discovery and Reuse". Bulletin of the ASIS&T. American Society for Information Science and Technology. Retrieved 11 January 2016.
  18. ^ Borne, Kirk. "Astroinformatics in a Nutshell". asaip.psu.edu. The Astrostatistics and Astroinformatics Portal, Penn State University. Retrieved 11 January 2016.
  19. ^ Feigelson, Eric. "Astrostatistics in a Nutshell". asaip.psu.edu. The Astrostatistics and Astroinformatics Portal, Penn State University. Retrieved 11 January 2016.
  20. ^ Feigelson, E.; Ivezić, Ž.; Hilbe, J.; Borne, K. (2013). "New Organizations to Support Astroinformatics and Astrostatistics". arXiv:1301.3069 [astro-ph.IM].
  21. ^ Borne, Kirk (2009). "The Revolution in Astronomy Education: Data Science for the Masses". arXiv:0909.3895 [astro-ph.IM].
  22. ^ "Using Data in the Classroom". Science Education Resource Center at Carleton College. National Science Digital Library. Retrieved 11 January 2016.
  23. ^ Borne, Kirk. Astroinformatics: Data-Oriented Astronomy (PDF). George Mason University, USA. Retrieved January 21, 2015.
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