Hostname: page-component-76fb5796d-5g6vh Total loading time: 0 Render date: 2024-04-27T03:44:52.121Z Has data issue: false hasContentIssue false
  • English
  • Français

The changing landscape of astrostatistics and astroinformatics

Published online by Cambridge University Press:  30 May 2017

Eric D. Feigelson
Eric D. Feigelson*
Affiliation:
Center for Astrostatistics and Department of Astronomy and Astrophysics, Pennsylvania State University, University Park PA 16802USA email: edf@astro.psu.edu
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The history and current status of the cross-disciplinary fields of astrostatistics and astroinformatics are reviewed. Astronomers need a wide range of statistical methods for both data reduction and science analysis. With the proliferation of high-throughput telescopes, efficient large scale computational methods are also becoming essential. However, astronomers receive only weak training in these fields during their formal education. Interest in the fields is rapidly growing with conferences organized by scholarly societies, textbooks and tutorial workshops, and research studies pushing the frontiers of methodology. R, the premier language of statistical computing, can provide an important software environment for the incorporation of advanced statistical and computational methodology into the astronomical community.

Keywords

data analysiscosmologystatisticscomputer sciencemachine learninghigh performance computingeducation
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S325: Astroinformatics , October 2016 , pp. 3 - 9
Copyright
Copyright © International Astronomical Union 2017 

References

Baddeley, A., Rubak, E., & Turner, R. 2015, Spatial Point Patterns: Methodology and Applications in R, CRC Press Google Scholar
Chernoff, H. & Lehmann, E. L. 1954, The Use of Maximum Likelihood Estimates in χ2 Tests for Goodness of Fit, Annals of Mathematical Statistics, 25 (3): 579–586 CrossRefGoogle Scholar
Feigelson, E. D. & Babu, G. J. 2012, Modern Statistical Methods for Astronomy with R Applications, Cambridge Univ. Press Google Scholar
Fisher, R. A. 1922, On the mathematical foundations of theoretical statistics, Phil. Trans. Royal Society, A, 222, 309–368 Google Scholar
Gregory, P. C. 2005, Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica Support, Cambridge Univ. Press Google Scholar
Ivezić, Z., Connolly, A. J., VanderPlas, J., & Gray, A. 2014, Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data, Princeton Univ. Press Google Scholar
Momcheva, I. & Tollerud, E. 2015, Software Use in Astronomy: an Informal Survey, https://www.authorea.com/users/10533/articles/18046 Google Scholar
Stephens, M. A. 1986, Tests Based on EDF Statistics, in D'Agostino, R. B. & Stephens, M. A. Goodness-of-Fit Techniques, New York: Marcel Dekker.Google Scholar
Wall, J. V. & Jenkins, C. R. 2012, Practical Statistics for Astronomers, 2nd ed., Cambridge Univ. Press Google Scholar