Sloan Digital Sky Survey III photometric quasar clustering: probing the initial conditions of the Universe
- McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213 (United States)
- Department of Physics and Astronomy, University of Wyoming, Laramie, WY 82071 (United States)
- Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94702 (United States)
- Institute of Cosmology and Gravitation, University of Portsmouth, Dennis Sciama Building, Portsmouth, PO1 3FX (United Kingdom)
- Department of Astronomy, Ohio State University, 140 West 18th Avenue, Columbus, OH 43210 (United States)
- Department of Physics and Astronomy, Yale University, New Haven, CT 06520 (United States)
- Brookhaven National Laboratory, Bldg. 510, Upton NY 11375 (United States)
- Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544 (United States)
- Department of Astronomy and Astrophysics, The Pennsylvania State University, University Park, PA 16802 (United States)
- Apache Point Observatory, P.O. Box 59, Sunspot, NM 88349-0059 (United States)
- CEA, Centre de Saclay, Irfu/SPP, F-91191 Gif-sur-Yvette (France)
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 0∼ 11,00 square degrees and probes a volume of 80 h{sup −3} Gpc{sup 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 redshift slices with an accuracy of ∼ 25% over a bin width of δ{sub l} ∼ 10−15 on scales corresponding to matter-radiation equality and larger (0ℓ ∼ 2−3). 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{sub NL} = −113{sup +154}{sub −154} (1σ 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 1σ ≅ 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.
- OSTI ID:
- 22525823
- Journal Information:
- Journal of Cosmology and Astroparticle Physics, Vol. 2015, Issue 05; Other Information: Country of input: International Atomic Energy Agency (IAEA); ISSN 1475-7516
- Country of Publication:
- United States
- Language:
- English
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