The next decade of astroinformatics and astrostatistics

Aneta Siemiginowska*, Gwendolyn Eadie, Ian Czekala, Eric Feigelson, Eric B. Ford, Vinay Kashyap, Michael Kuhn, Tom Loredo, Michelle Ntampaka, Abbie Stevens, Arturo Avelino, Kirk Borne, Tamas Budavari, Blakesley Burkhart, Jessi Cisewski-Kehe, Francesca Civano, Igor Chilingarian, David A. van Dyk, Giuseppina Fabbiano, Douglas P. FinkbeinerDaniel Foreman-Mackey, Peter Freeman, Antonella Fruscione, Alyssa A. Goodman, Matthew Graham, Hans Moritz Guenther, Jon Hakkila, Lars Hernquist, Daniela Huppenkothen, David J. James, Casey Law, Joseph Lazio, Thomas Lee, Mercedes López-Morales, Ashish A. Mahabal, Kaisey Mandel, Xiao-Li Meng, John Moustakas, Demitri Muna, J. E. G. Peek, Gordon Richards, Stephen K. N. Portillo, Jeff Scargle, Rafael S. de Souza, Joshua S. Speagle, Keivan G. Stassun, David C. Stenning, Stephen R. Taylor, Grant R. Tremblay, Virginia Trimble, Padma A. Yanamandra-Fisher, C. Alex Young

*Corresponding author for this work

Research output: Contribution to journalArticle

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.
Original languageEnglish
Article number355
Number of pages15
JournalBulletin of the American Astronomical Society
Volume51
Issue number3
Publication statusPublished - 31 May 2019

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