Density split statistics: Cosmological constraints from counts and lensing in cells in DES Y1 and SDSS data

Desai, Shantanu and Krause, E and David, C and et al, . (2018) Density split statistics: Cosmological constraints from counts and lensing in cells in DES Y1 and SDSS data. Physical Review D, 98 (2). ISSN 2470-0010

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Abstract

We derive cosmological constraints from the probability distribution function (PDF) of evolved large-scale matter density fluctuations. We do this by splitting lines of sight by density based on their count of tracer galaxies, and by measuring both gravitational shear around and counts-in-cells in overdense and underdense lines of sight, in Dark Energy Survey (DES) First Year and Sloan Digital Sky Survey (SDSS) data. Our analysis uses a perturbation theory model [O. Friedrich et al., Phys. Rev. D 98, 023508 (2018)] and is validated using N -body simulation realizations and log-normal mocks. It allows us to constrain cosmology, bias and stochasticity of galaxies with respect to matter density and, in addition, the skewness of the matter density field. From a Bayesian model comparison, we find that the data weakly prefer a connection of galaxies and matter that is stochastic beyond Poisson fluctuations on ≤ 20 arcmin angular smoothing scale. The two stochasticity models we fit yield DES constraints on the matter density Ω m = 0.2 6 + 0.04 − 0.03 and Ω m = 0.2 8 + 0.05 − 0.04 that are consistent with each other. These values also agree with the DES analysis of galaxy and shear two-point functions (3x2pt, DES Collaboration et al.) that only uses second moments of the PDF. Constraints on σ 8 are model dependent ( σ 8 = 0.9 7 + 0.07 − 0.06 and 0.8 0 + 0.06 − 0.07 for the two stochasticity models), but consistent with each other and with the 3 x 2pt results if stochasticity is at the low end of the posterior range. As an additional test of gravity, counts and lensing in cells allow to compare the skewness S 3 of the matter density PDF to its Λ CDM prediction. We find no evidence of excess skewness in any model or data set, with better than 25 per cent relative precision in the skewness estimate from DES alone.

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