Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-25T01:17:42.411Z Has data issue: false hasContentIssue false

Simulation of the analysis of interferometric microwave background polarization data

Published online by Cambridge University Press:  01 July 2015

Emory F. Bunn
Affiliation:
University of Richmond, USA email: [email protected]
Ata Karakci
Affiliation:
Brown University, USA
Paul M. Sutter
Affiliation:
Ohio State University, USA Institut d'Astrophysique de Paris, France
Le Zhang
Affiliation:
University of Wisconsin – Madison, USA
Gregory S. Tucker
Affiliation:
Brown University, USA
Peter T. Timbie
Affiliation:
University of Wisconsin – Madison, USA
Benjamin D. Wandelt
Affiliation:
Institut d'Astrophysique de Paris, France
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.

We present results from an end-to-end simulation pipeline of interferometric observations of cosmic microwave background polarization. We use both maximum-likelihood and Gibbs sampling techniques to estimate the power spectrum. In addition, we use Gibbs sampling for image reconstruction from interferometric visibilities. The results indicate the level to which various systematic errors (e.g., pointing errors, gain errors, beam shape errors, cross polarization) must be controlled in order to successfully detect and characterize primordial B modes and achieve other scientific goals. In addition, we show that Gibbs sampling is an effective method of image reconstruction for interferometric data in other astrophysical contexts.

Type
Contributed Papers
Copyright
Copyright © International Astronomical Union 2015 

References

Ade, P. A. R., et al. 2014, Phys. Rev. Lett., 112, 241101CrossRefGoogle Scholar
Bunn, E. F. 2007, Phys. Rev. D, 75, 083517CrossRefGoogle Scholar
Ghribi, A., et al. 2013, JLTP, 173Google Scholar
Hobson, M. P. & Maisinger, K. 2002, MNRAS, 334, 569Google Scholar
Högbom, J. A. 1974, A&AS, 15, 417Google Scholar
Hu, W. & Dodelson, S. 2002, ARAA, 40, 171Google Scholar
Jaeger, et al. 2008, in Argyle, R. W., Bunclark, P. S., & Lewis, J. R., eds, Astronomical Data Analysis Software and Systems XVII, Vol. 394 of Astronomical Society of the Pacific Conference Series, The Common Astronomy Software Application (CASA), p. 623Google Scholar
Karakci, A., Sutter, P. M., Zhang, L., Bunn, E. F., Korotkov, A., Timbie, P., Tucker, G. S., & Wandelt, B. D. 2013, ApJS, 204, 10CrossRefGoogle Scholar
Karakci, A., Zhang, L., Sutter, P. M., Bunn, E. F., Korotkov, A., Timbie, P., Tucker, G. S., & Wandelt, B. D. 2013, ApJS, 207, 14Google Scholar
Larson, D. L., Eriksen, H. K., Wandelt, B. D., Górski, K. M., Huey, G., Jewell, J. B., & O'Dwyer, I. J. 2007, ApJ, 656, 653Google Scholar
Sutter, P. M., Wandelt, B. D., & Malu, S. S. 2012, ApJS, 202, 9Google Scholar
Sutter, P. M., et al. 2014, MNRAS, 438, 768CrossRefGoogle Scholar
Timbie, P. T., et al. 2006, New Astron. Revs, 50, 999Google Scholar
Wiaux, Y., Jacques, L., Puy, G., Scaife, A. M. M.., & Vandergheynst, P. 2009, MNRAS, 395, 1733Google Scholar
Zhang, L., Karakci, A., Sutter, P. M., Bunn, E. F., Korotkov, A., Timbie, P., Tucker, G. S., & Wandelt, B. D. 2013, ApJS, 206, 24CrossRefGoogle Scholar