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The XFaster power spectrum and likelihood estimator for the analysis of cosmic microwave background maps

Gambrel, A. E., Rahlin, A. S., Song, X., Contaldi, C. R., Ade, P. A. R. ORCID: https://orcid.org/0000-0002-5127-0401, Amiri, M., Benton, S. J., Bergman, A. S., Bihary, R., Bock, J. J., Bond, J. R., Bonetti, J. A., Bryan, S. A., Chiang, H. C., Duivenvoorden, A. J., Eriksen, H. K., Farhang, M., Filippini, J. P., Fraisse, A. A., Freese, K., Galloway, M., Gandilo, N. N., Gualtieri, R., Gudmundsson, J. E., Halpern, M., Hartley, J., Hasselfield, M., Hilton, G., Holmes, W., Hristov, V. V., Huang, Z., Irwin, K. D., Jones, W. C., Karakci, A., Kuo, C. L., Kermish, Z. D., Leung, J. S.-Y., Li, S., Mak, D. S. Y., Mason, P. V., Megerian, K., Moncelsi, L., Morford, T. A., Nagy, J. M., Netterfield, C. B., Nolta, M., O'Brient, R., Osherson, B., Padilla, I. L., Racine, B., Reintsema, C., Ruhl, J. E., Ruud, T. M., Shariff, J. A., Shaw, E. C., Shiu, C., Soler, J. D., Trangsrud, A., Tucker, C. ORCID: https://orcid.org/0000-0002-1851-3918, Tucker, R. S., Turner, A. D., List, J. F. van der, Weber, A. C., Wehus, I. K., Wen, S., Wiebe, D. V. and Young, E. Y. 2021. The XFaster power spectrum and likelihood estimator for the analysis of cosmic microwave background maps. Astrophysical Journal 922 (2) , 132. 10.3847/1538-4357/ac230b

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Abstract

We present the XFaster analysis package, a fast, iterative angular power spectrum estimator based on a diagonal approximation to the quadratic Fisher matrix estimator. It uses Monte Carlo simulations to compute noise biases and filter transfer functions and is thus a hybrid of both Monte Carlo and quadratic estimator methods. In contrast to conventional pseudo-Cℓ–based methods, the algorithm described here requires a minimal number of simulations and does not require them to be precisely representative of the data to estimate accurate covariance matrices for the bandpowers. The formalism works with polarization-sensitive observations and also data sets with identical, partially overlapping, or independent survey regions. The method was first implemented for the analysis of BOOMERanG data and also used as part of the Planck analysis. Here we describe the full, publicly available analysis package, written in Python, as developed for the analysis of data from the 2015 flight of the Spider instrument. The package includes extensions for self-consistently estimating null spectra and estimating fits for Galactic foreground contributions. We show results from the extensive validation of XFaster using simulations and its application to the Spider data set.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Physics and Astronomy
Publisher: American Astronomical Society
ISSN: 0004-637X
Date of First Compliant Deposit: 15 February 2022
Date of Acceptance: 24 August 2021
Last Modified: 09 Jan 2023 04:19
URI: https://orca.cardiff.ac.uk/id/eprint/147473

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