How to estimate the 3D power spectrum of the Lyman-α forest
Abstract
Here, we derive and numerically implement an algorithm for estimating the 3D power spectrum of the Lyman-α (Lyα) forest flux fluctuations. The algorithm exploits the unique geometry of Lyα forest data to efficiently measure the cross-spectrum between lines of sight as a function of parallel wavenumber, transverse separation and redshift. We start by approximating the global covariance matrix as block-diagonal, where only pixels from the same spectrum are correlated. We then compute the eigenvectors of the derivative of the signal covariance with respect to cross-spectrum parameters, and project the inverse-covariance-weighted spectra onto them. This acts much like a radial Fourier transform over redshift windows. The resulting cross-spectrum inference is then converted into our final product, an approximation of the likelihood for the 3D power spectrum expressed as second order Taylor expansion around a fiducial model. We demonstrate the accuracy and scalability of the algorithm and comment on possible extensions. Our algorithm will allow efficient analysis of the upcoming Dark Energy Spectroscopic Instrument dataset.
- Authors:
-
- Univ. College London, Bloomsbury (United Kingdom). Dept. of Physics and Astronomy
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Publication Date:
- Research Org.:
- Brookhaven National Lab. (BNL), Upton, NY (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25); Univ. College London, Bloomsbury (United Kingdom)
- OSTI Identifier:
- 1424960
- Alternate Identifier(s):
- OSTI ID: 1530316
- Report Number(s):
- BNL-200052-2018-JAAM
Journal ID: ISSN 1475-7516; TRN: US1801980
- Grant/Contract Number:
- SC0012704; ST/N003853/1; AC02-05CH11231
- Resource Type:
- Journal Article: Accepted Manuscript
- Journal Name:
- Journal of Cosmology and Astroparticle Physics
- Additional Journal Information:
- Journal Volume: 2018; Journal Issue: 01; Journal ID: ISSN 1475-7516
- Publisher:
- Institute of Physics (IOP)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 79 ASTRONOMY AND ASTROPHYSICS
Citation Formats
Font-Ribera, Andreu, McDonald, Patrick, and Slosar, Anže. How to estimate the 3D power spectrum of the Lyman-α forest. United States: N. p., 2018.
Web. doi:10.1088/1475-7516/2018/01/003.
Font-Ribera, Andreu, McDonald, Patrick, & Slosar, Anže. How to estimate the 3D power spectrum of the Lyman-α forest. United States. doi:10.1088/1475-7516/2018/01/003.
Font-Ribera, Andreu, McDonald, Patrick, and Slosar, Anže. Tue .
"How to estimate the 3D power spectrum of the Lyman-α forest". United States. doi:10.1088/1475-7516/2018/01/003. https://www.osti.gov/servlets/purl/1424960.
@article{osti_1424960,
title = {How to estimate the 3D power spectrum of the Lyman-α forest},
author = {Font-Ribera, Andreu and McDonald, Patrick and Slosar, Anže},
abstractNote = {Here, we derive and numerically implement an algorithm for estimating the 3D power spectrum of the Lyman-α (Lyα) forest flux fluctuations. The algorithm exploits the unique geometry of Lyα forest data to efficiently measure the cross-spectrum between lines of sight as a function of parallel wavenumber, transverse separation and redshift. We start by approximating the global covariance matrix as block-diagonal, where only pixels from the same spectrum are correlated. We then compute the eigenvectors of the derivative of the signal covariance with respect to cross-spectrum parameters, and project the inverse-covariance-weighted spectra onto them. This acts much like a radial Fourier transform over redshift windows. The resulting cross-spectrum inference is then converted into our final product, an approximation of the likelihood for the 3D power spectrum expressed as second order Taylor expansion around a fiducial model. We demonstrate the accuracy and scalability of the algorithm and comment on possible extensions. Our algorithm will allow efficient analysis of the upcoming Dark Energy Spectroscopic Instrument dataset.},
doi = {10.1088/1475-7516/2018/01/003},
journal = {Journal of Cosmology and Astroparticle Physics},
issn = {1475-7516},
number = 01,
volume = 2018,
place = {United States},
year = {2018},
month = {1}
}
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Figures / Tables:
FIG. 1.: S̃,α matrices for two different parameters. Left: eighth k-bin (k‖ = 0.0015s km −1) of the second z-bin (z = 2.15); Right: first non-zero k-bin (k‖ = 0.0002s km −1) of the fifth z-bin (z = 2.88). The redshift bin (z-bin) of the parameter sets the diagonal bandmore »
Figures / Tables found in this record: