The XFaster Power Spectrum and Likelihood Estimator for the Analysis of Cosmic Microwave Background Maps

A. E. Gambrel; A. S. Rahlin; X. Song; C. R. Contaldi; P. A.R. Ade; M. Amiri; S. J. Benton; A. S. Bergman; R. Bihary; J. J. Bock; J. R. Bond; J. A. Bonetti; S. A. Bryan; H. C. Chiang; A. J. Duivenvoorden; H. K. Eriksen; M. Farhang; J. P. Filippini; A. A. Fraisse; K. Freese; M. Galloway; N. N. Gandilo; R. Gualtieri; J. E. Gudmundsson; M. Halpern; J. Hartley; M. Hasselfield; G. Hilton; W. Holmes; V. V. Hristov; Z. Huang; K. D. Irwin; W. C. Jones; A. Karakci; C. L. Kuo; Z. D. Kermish; J. S.Y. Leung; S. Li; D. S.Y. Mak; P. V. Mason; K. Megerian; L. Moncelsi; T. A. Morford; J. M. Nagy; C. B. Netterfield; M. Nolta; R. O'Brient; B. Osherson; I. L. Padilla; B. Racine; C. Reintsema; J. E. Ruhl; T. M. Ruud; J. A. Shariff; E. C. Shaw; C. Shiu; J. D. Soler; A. Trangsrud; C. Tucker; R. S. Tucker; A. D. Turner; J. F.Van Der List; A. C. Weber; I. K. Wehus; S. Wen; D. V. Wiebe; E. Y. Young

doi:10.3847/1538-4357/ac230b

The XFaster Power Spectrum and Likelihood Estimator for the Analysis of Cosmic Microwave Background Maps

A. E. Gambrel, A. S. Rahlin, X. Song, C. R. Contaldi, P. A.R. Ade, M. Amiri, S. J. Benton, A. S. Bergman, R. Bihary, J. J. Bock, J. R. Bond, J. A. Bonetti, S. A. Bryan, H. C. Chiang, A. J. Duivenvoorden, H. K. Eriksen, M. Farhang, J. P. Filippini, A. A. Fraisse, K. FreeseM. Galloway, N. N. Gandilo, R. Gualtieri, J. E. Gudmundsson, M. Halpern, J. Hartley, M. Hasselfield, G. Hilton, W. Holmes, V. V. Hristov, Z. Huang, K. D. Irwin, W. C. Jones, A. Karakci, C. L. Kuo, Z. D. Kermish, J. S.Y. Leung, S. Li, D. S.Y. Mak, P. V. Mason, K. Megerian, L. Moncelsi, T. A. Morford, J. M. Nagy, C. B. Netterfield, M. Nolta, R. O'Brient, B. Osherson, I. L. Padilla, B. Racine, C. Reintsema, J. E. Ruhl, T. M. Ruud, J. A. Shariff, E. C. Shaw, C. Shiu, J. D. Soler, A. Trangsrud, C. Tucker, R. S. Tucker, A. D. Turner, J. F.Van Der List, A. C. Weber, I. K. Wehus, S. Wen, D. V. Wiebe, E. Y. Young

Earth and Space Exploration, School of (SESE)

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

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-C ℓ -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.

Original language	English (US)
Article number	132
Journal	Astrophysical Journal
Volume	922
Issue number	2
DOIs	https://doi.org/10.3847/1538-4357/ac230b
State	Published - Dec 1 2021

ASJC Scopus subject areas

Astronomy and Astrophysics
Space and Planetary Science

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Gambrel, A. E., Rahlin, A. S., Song, X., Contaldi, C. R., Ade, P. A. R., 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., ... Young, E. Y. (2021). The XFaster Power Spectrum and Likelihood Estimator for the Analysis of Cosmic Microwave Background Maps. Astrophysical Journal, 922(2), Article 132. https://doi.org/10.3847/1538-4357/ac230b

Gambrel, AE, Rahlin, AS, Song, X, Contaldi, CR, Ade, PAR, Amiri, M, Benton, SJ, Bergman, AS, Bihary, R, Bock, JJ, Bond, JR, Bonetti, JA, Bryan, SA, Chiang, HC, Duivenvoorden, AJ, Eriksen, HK, Farhang, M, Filippini, JP, Fraisse, AA, Freese, K, Galloway, M, Gandilo, NN, Gualtieri, R, Gudmundsson, JE, Halpern, M, Hartley, J, Hasselfield, M, Hilton, G, Holmes, W, Hristov, VV, Huang, Z, Irwin, KD, Jones, WC, Karakci, A, Kuo, CL, Kermish, ZD, Leung, JSY, Li, S, Mak, DSY, Mason, PV, Megerian, K, Moncelsi, L, Morford, TA, Nagy, JM, Netterfield, CB, Nolta, M, O'Brient, R, Osherson, B, Padilla, IL, Racine, B, Reintsema, C, Ruhl, JE, Ruud, TM, Shariff, JA, Shaw, EC, Shiu, C, Soler, JD, Trangsrud, A, Tucker, C, Tucker, RS, Turner, AD, List, JFVD, Weber, AC, Wehus, IK, Wen, S, Wiebe, DV & Young, EY 2021, 'The XFaster Power Spectrum and Likelihood Estimator for the Analysis of Cosmic Microwave Background Maps', Astrophysical Journal, vol. 922, no. 2, 132. https://doi.org/10.3847/1538-4357/ac230b

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title = "The XFaster Power Spectrum and Likelihood Estimator for the Analysis of Cosmic Microwave Background Maps",

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-C ℓ -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.",

author = "Gambrel, {A. E.} and Rahlin, {A. S.} and X. Song and Contaldi, {C. R.} and Ade, {P. A.R.} and M. Amiri and Benton, {S. J.} and Bergman, {A. S.} and R. Bihary and Bock, {J. J.} and Bond, {J. R.} and Bonetti, {J. A.} and Bryan, {S. A.} and Chiang, {H. C.} and Duivenvoorden, {A. J.} and Eriksen, {H. K.} and M. Farhang and Filippini, {J. P.} and Fraisse, {A. A.} and K. Freese and M. Galloway and Gandilo, {N. N.} and R. Gualtieri and Gudmundsson, {J. E.} and M. Halpern and J. Hartley and M. Hasselfield and G. Hilton and W. Holmes and Hristov, {V. V.} and Z. Huang and Irwin, {K. D.} and Jones, {W. C.} and A. Karakci and Kuo, {C. L.} and Kermish, {Z. D.} and Leung, {J. S.Y.} and S. Li and Mak, {D. S.Y.} and Mason, {P. V.} and K. Megerian and L. Moncelsi and Morford, {T. A.} and Nagy, {J. M.} and Netterfield, {C. B.} and M. Nolta and R. O'Brient and B. Osherson and Padilla, {I. L.} and B. Racine and C. Reintsema and Ruhl, {J. E.} and Ruud, {T. M.} and Shariff, {J. A.} and Shaw, {E. C.} and C. Shiu and Soler, {J. D.} and A. Trangsrud and C. Tucker and Tucker, {R. S.} and Turner, {A. D.} and List, {J. F.Van Der} and Weber, {A. C.} and Wehus, {I. K.} and S. Wen and Wiebe, {D. V.} and Young, {E. Y.}",

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doi = "10.3847/1538-4357/ac230b",

language = "English (US)",

volume = "922",

journal = "Astrophysical Journal",

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TY - JOUR

T1 - The XFaster Power Spectrum and Likelihood Estimator for the Analysis of Cosmic Microwave Background Maps

AU - Gambrel, A. E.

AU - Rahlin, A. S.

AU - Song, X.

AU - Contaldi, C. R.

AU - Ade, P. A.R.

AU - Amiri, M.

AU - Benton, S. J.

AU - Bergman, A. S.

AU - Bihary, R.

AU - Bock, J. J.

AU - Bond, J. R.

AU - Bonetti, J. A.

AU - Bryan, S. A.

AU - Chiang, H. C.

AU - Duivenvoorden, A. J.

AU - Eriksen, H. K.

AU - Farhang, M.

AU - Filippini, J. P.

AU - Fraisse, A. A.

AU - Freese, K.

AU - Galloway, M.

AU - Gandilo, N. N.

AU - Gualtieri, R.

AU - Gudmundsson, J. E.

AU - Halpern, M.

AU - Hartley, J.

AU - Hasselfield, M.

AU - Hilton, G.

AU - Holmes, W.

AU - Hristov, V. V.

AU - Huang, Z.

AU - Irwin, K. D.

AU - Jones, W. C.

AU - Karakci, A.

AU - Kuo, C. L.

AU - Kermish, Z. D.

AU - Leung, J. S.Y.

AU - Li, S.

AU - Mak, D. S.Y.

AU - Mason, P. V.

AU - Megerian, K.

AU - Moncelsi, L.

AU - Morford, T. A.

AU - Nagy, J. M.

AU - Netterfield, C. B.

AU - Nolta, M.

AU - O'Brient, R.

AU - Osherson, B.

AU - Padilla, I. L.

AU - Racine, B.

AU - Reintsema, C.

AU - Ruhl, J. E.

AU - Ruud, T. M.

AU - Shariff, J. A.

AU - Shaw, E. C.

AU - Shiu, C.

AU - Soler, J. D.

AU - Trangsrud, A.

AU - Tucker, C.

AU - Tucker, R. S.

AU - Turner, A. D.

AU - List, J. F.Van Der

AU - Weber, A. C.

AU - Wehus, I. K.

AU - Wen, S.

AU - Wiebe, D. V.

AU - Young, E. Y.

PY - 2021/12/1

Y1 - 2021/12/1

N2 - 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-C ℓ -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.

AB - 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-C ℓ -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.

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UR - http://www.scopus.com/inward/citedby.url?scp=85120804957&partnerID=8YFLogxK

U2 - 10.3847/1538-4357/ac230b

DO - 10.3847/1538-4357/ac230b

M3 - Article

AN - SCOPUS:85120804957

SN - 0004-637X

VL - 922

JO - Astrophysical Journal

JF - Astrophysical Journal

IS - 2

M1 - 132

ER -

The XFaster Power Spectrum and Likelihood Estimator for the Analysis of Cosmic Microwave Background Maps

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