The Lyα Forest Power Spectrum from the Sloan Digital Sky Survey

By P. Mcdonald, Uros Seljak, S. Burles, David Schlegel, D. Weinberg, Renyue Cen, David Shih, Joop Schaye, D. Schneider, N. Bahcall and 9 more. 2004

273 citations recorded by the source corpus.

We measure the power spectrum, PF(k, z), of the transmitted flux in the Lyα forest using 3035 high-redshift quasar spectra from the Sloan Digital Sky Survey. This sample is almost 2 orders of magnitude larger than any previously available data set, yielding statistical errors of ~0.6% and ~0.005 on, respectively, the overall amplitude and logarithmic slope of PF(k, z). This unprecedented statistical power requires a correspondingly careful analysis of the data and of possible systematic contaminations in it. For this purpose we reanalyze the raw spectra to make use of information not preserved by the standard pipeline. We investigate the details of the noise in the data, resolution of the spectrograph, sky subtraction, quasar continuum, and metal absorption. We find that background sources such as metals contribute significantly to the total power and have to be subtracted properly. We also find clear evidence for Si III correlations with the Lyα forest and suggest a simple model to account for this contribution to the power. Our tests indicate that any residual systematics in the analysis are unlikely to affect the inference of cosmological parameters from PF(k, z). These results should provide an essential ingredient for all future attempts to constrain modeling of structure formation, cosmological parameters, and theories for the origin of primordial fluctuations.

Subfields: Astronomy and Astrophysics, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/a43413e5d7b5e74554c5bf0cc2442862e3a7afdc

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Citations in the corpus

The one-dimensional Lyα forest power spectrum from BOSS (2013, methodology): “The second approach uses a likelihood method to compute the covariance matrix of δ in real space (or line-of-sight correlation function) as a function of the pixel pair separation in the spectra (McDonald et al. 2006).”
PROBING THE INTERGALACTIC MEDIUM/GALAXY CONNECTION. V. ON THE ORIGIN OF Lyα AND O vi ABSORPTION AT z < 0.2 (2011, background): “Furthermore, the IGM is expected to trace the dark matter distribution of large-scale structure, offering unique constraints on our cosmological paradigm (e.g. Miralda-Escudé et al. 1996; Rauch 1998; McDonald et al. 2006; Viel et al. 2009).”
Baryon acoustic oscillations in the Lyα forest of BOSS DR11 quasars (2014, background): “The weights should obviously favor low-noise pixels and take into account the redshift dependence of the pixel correlations, ξi j(z) ∝ (1 + zi)γ/2(1 + z j)γ/2, with γ ∼ 3.8 (McDonald et al., 2006).”
Cosmology from cosmic shear power spectra with Subaru Hyper Suprime-Cam first-year data (2018, background): “…galaxies (e.g., Cole et al. 2005; Yamamoto et al. 2006; Percival et al. 2010; Reid et al. 2010; Blake et al. 2011; Oka et al. 2014; Alam et al. 2017; Beutler et al. 2017), and the Lyman-α forest (e.g., McDonald et al. 2006; Palanque-Delabrouille et al. 2013; Viel et al. 2013; Iršič et al. 2017).”
Baryon acoustic oscillations from the complete SDSS-III Ly$\alpha$-quasar cross-correlation function at $z=2.4$ (2017, methodology): “(25) For Lyα absorption we assume bLyα(z) = bLyα(2.4)[(1 + z)/(1 + 2.4)]αLyα , (26) where αLyα = 2.9 as observed in measurements of the fluxcorrelation, ξ f f ,1D, within individual forests (McDonald et al. 2006).”
Lyman-alpha constraints on warm and on warm-plus-cold dark matter models (2008, methodology): “In the following, we will use the flux power provided by the SDSS collaboration, introducing nuisance parameters for the resolution and noise in each redshift bin as suggest by [96, 61], and implicitly assuming that all the contaminants above have been either removed or properly modelled in their an”
The Completed SDSS-IV Extended Baryon Oscillation Spectroscopic Survey: Baryon Acoustic Oscillations with Lyα Forests (2020, methodology): “The weights wi and wj are defined by Equation (7), where the factor + gz1( ) g =a 2.9Ly is chosen to favor high-redshift pixels where the amplitude of the correlation function is greatest (McDonald et al. 2006).”