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The Next Decade of Astroinformatics and Astrostatistics

Siemiginowska, Aneta and Kuhn, Michael and Graham, Matthew and Mahabal, Ashish A. and Taylor, Stephen R. (2019) The Next Decade of Astroinformatics and Astrostatistics. Astro2020 Science White Paper, . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20191119-083352740

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Abstract

Over the past century, major advances in astronomy and astrophysics have been largely driven by improvements in instrumentation and data collection. With the amassing of high quality data from new telescopes, and especially with the advent of deep and large astronomical surveys, it is becoming clear that future advances will also rely heavily on how those data are analyzed and interpreted. New methodologies derived from advances in statistics, computer science, and machine learning are beginning to be employed in sophisticated investigations that are not only bringing forth new discoveries, but are placing them on a solid footing. Progress in wide-field sky surveys, interferometric imaging, precision cosmology, exoplanet detection and characterization, and many subfields of stellar, Galactic and extragalactic astronomy, has resulted in complex data analysis challenges that must be solved to perform scientific inference. Research in astrostatistics and astroinformatics will be necessary to develop the state-of-the-art methodology needed in astronomy. Overcoming these challenges requires dedicated, interdisciplinary research. We recommend: (1) increasing funding for interdisciplinary projects in astrostatistics and astroinformatics; (2) dedicating space and time at conferences for interdisciplinary research and promotion; (3) developing sustainable funding for long-term astrostatisics appointments; and (4) funding infrastructure development for data archives and archive support, state-of-the-art algorithms, and efficient computing.


Item Type:Report or Paper (White Paper)
Related URLs:
URLURL TypeDescription
https://arxiv.org/abs/1903.06796arXivDiscussion Paper
ORCID:
AuthorORCID
Kuhn, Michael0000-0002-0631-7514
Graham, Matthew0000-0002-3168-0139
Mahabal, Ashish A.0000-0003-2242-0244
Taylor, Stephen R.0000-0003-0264-1453
Group:TAPIR
Series Name:Astro2020 Science White Paper
Record Number:CaltechAUTHORS:20191119-083352740
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20191119-083352740
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:99921
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:19 Nov 2019 22:11
Last Modified:19 Nov 2019 22:11

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