Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-21T18:35:56.763Z Has data issue: false hasContentIssue false
  • English
  • Français

Bayesian component separation: The Planck experience

Published online by Cambridge University Press:  08 May 2018

Ingunn Kathrine Wehus Open the ORCID record for Ingunn Kathrine Wehus [Opens in a new window]  and
Hans Kristian Eriksen
Ingunn Kathrine Wehus
Affiliation:
Institute of Theoretical Astrophysics, University of Oslo, Postboks 1029 Blindern, 0315 Oslo, Norway email: [email protected]
Hans Kristian Eriksen
Affiliation:
Institute of Theoretical Astrophysics, University of Oslo, Postboks 1029 Blindern, 0315 Oslo, Norway email: [email protected]
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.

Bayesian component separation techniques have played a central role in the data reduction process of Planck. The most important strength of this approach is its global nature, in which a parametric and physical model is fitted to the data. Such physical modeling allows the user to constrain very general data models, and jointly probe cosmological, astrophysical and instrumental parameters. This approach also supports statistically robust goodness-of-fit tests in terms of data-minus-model residual maps, which are essential for identifying residual systematic effects in the data. The main challenges are high code complexity and computational cost. Whether or not these costs are justified for a given experiment depends on its final uncertainty budget. We therefore predict that the importance of Bayesian component separation techniques is likely to increase with time for intensity mapping experiments, similar to what has happened in the CMB field, as observational techniques mature, and their overall sensitivity improves.

Keywords

Cosmology: cosmic microwave backgroundGalaxy: generalMethods: data analysis
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S333: Peering towards Cosmic Dawn , October 2017 , pp. 274 - 279
Copyright
Copyright © International Astronomical Union 2018 

References

Eriksen, H. K., O’Dwyer, I. J., Jewell, J. B., et al. 2004 ApJS, 155, 227CrossRefGoogle Scholar
Eriksen, H. K., Jewell, J. B., Dickinson, C., et al. 2008, ApJ, 676, 10–32CrossRefGoogle Scholar
Jewell, J. B., Levin, S., & Anderson, C. H., 2004, ApJ, 609, 1CrossRefGoogle Scholar
Leach, S., et al. 2008, A&A, 491, 597Google Scholar
Li, T. Y., Wechsler, R., Devaraj, K., & Church, S. E., 2016, ApJ, 817, 2CrossRefGoogle Scholar
Planck Collaboration, 2016, A&A, 594, A1Google Scholar
Planck Collaboration, 2016, A&A, 594, A8Google Scholar
Planck Collaboration, 2016, A&A, 594, A10Google Scholar
Planck Collaboration, 2016, A&A, 594, A13Google Scholar
Planck Collaboration, 2016, A&A, 594, A15Google Scholar
Planck Collaboration, 2016, A&A, 594, A17Google Scholar
Wandelt, B. D., Larson, D. L., & Lakshminarayanan, A., 2004, Phys Rev D, 70, 083511CrossRefGoogle Scholar