Journal
JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS
Volume -, Issue 5, Pages -Publisher
IOP PUBLISHING LTD
DOI: 10.1088/1475-7516/2015/05/040
Keywords
power spectrum; redshift surveys; inflation; cosmological parameters from LSS
Funding
- New Frontiers in Astronomy and Cosmology program at the John Templeton Foundation
- RESCEU fellowship
- Seaborg and Chamberlain Fellowship (via Lawrence Berkeley National Laboratory)
- McWilliams fellowship of the Bruce
- Astrid McWilliams Center for Cosmology
- NSF [1211112]
- NASA ADAP award [NNX12AE38G]
- Alfred P. Sloan Foundation
- National Science Foundation
- U.S. Department of Energy Office of Science
- University of Arizona
- Brazilian Participation Group
- Brookhaven National Laboratory
- University of Cambridge
- Carnegie Mellon University
- University of Florida
- French Participation Group
- German Participation Group
- Instituto de Astrofisica de Canarias
- Michigan State/Notre Dame/JINA Participation Group
- Johns Hopkins University
- Lawrence Berkeley National Laboratory
- Max Planck Institute for Astrophysics
- New Mexico State University
- New York University
- Ohio State University
- Pennsylvania State University
- University of Portsmouth
- Princeton University
- Spanish Participation Group
- University of Tokyo
- University of Utah
- Vanderbilt University
- University of Virginia
- University of Washington
- Yale University
- Science and Technology Facilities Council [ST/L00481X/1] Funding Source: researchfish
- Division Of Astronomical Sciences
- Direct For Mathematical & Physical Scien [1211112] Funding Source: National Science Foundation
- STFC [ST/L00481X/1] Funding Source: UKRI
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The Sloan Digital Sky Survey has surveyed 14,555 square degrees of the sky, and delivered over a trillion pixels of imaging data. We present the large-scale clustering of 1.6 million quasars between z = 0 : 5 and z = 2 : 5 that have been classified from this imaging, representing the highest density of quasars ever studied for clustering measurements. This data set spans similar to 11; 000 square degrees and probes a volume of 80 h(-3) Gpc(3). In principle, such a large volume and medium density of tracers should facilitate high-precision cosmological constraints. We measure the angular clustering of photometrically classified quasars using an optimal quadratic estimator in four redshiftslices with an accuracy of similar to 25% over a bin width of delta(l) similar to 10 - 15 on scales corresponding to matter-radiation equality and larger (l similar to 2 30). Observational systematics can strongly bias clustering measurements on large scales, which can mimic cosmologically relevant signals such as deviations from Gaussianity in the spectrum of primordial perturbations. We account for systematics by employing a new method recently proposed by Agarwal et al. (2014) to the clustering of photometrically classified quasars. We carefully apply our methodology to mitigate known observational systematics and further remove angular bins that are contaminated by unknown systematics. Combining quasar data with the photometric luminous red galaxy (LRG) sample of Ross et al. (2011) and Ho et al. (2012), and marginalizing over all bias and shot noise-like parameters, we obtain a constraint on local primordial non-Gaussianity of f(NL) = 113(-154)(+154) (1 sigma error). We next assume that the bias of quasar and galaxy distributions can be obtained independently from quasar/galaxy-CMB lensing cross-correlation measurements (such as those in Sherwin et al. (2013)). This can be facilitated by spectroscopic observations of the sources, enabling the redshift distribution to be completely determined, and allowing precise estimates of the bias parameters. In this paper, if the bias and shot noise parameters are fixed to their known values (which we model by fixing them to their best-fit Gaussian values), we find that the error bar reduces to 1 sigma similar or equal to 65. We expect this error bar to reduce further by at least another factor of five if the data is free of any observational systematics. We therefore emphasize that in order to make best use of large scale structure data we need an accurate modeling of known systematics, a method to mitigate unknown systematics, and additionally independent theoretical models or observations to probe the bias of dark matter halos.
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