Cosmological constraints from the convergence 1-point probability distribution

Kenneth Patton; Jonathan Blazek; Klaus Honscheid; Eric Huff; Peter Melchior; Ashley J. Ross; Eric Suchyta

doi:10.1093/mnras/stx1626

Cosmological constraints from the convergence 1-point probability distribution

Kenneth Patton, Jonathan Blazek, Klaus Honscheid, Eric Huff, Peter Melchior, Ashley J. Ross, Eric Suchyta

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Abstract

We examine the cosmological information available from the 1-point probability density function (PDF) of the weak-lensing convergence field, utilizing fast L-PICOLA simulations and a Fisher analysis. We find competitive constraints in the Ω_m-σ₈ plane from the convergence PDF with 188 arcmin² pixels compared to the cosmic shear power spectrum with an equivalent number of modes (ℓ < 886). The convergence PDF also partially breaks the degeneracy cosmic shear exhibits in that parameter space. A joint analysis of the convergence PDF and shear 2-point function also reduces the impact of shape measurement systematics, to which the PDF is less susceptible, and improves the total figure of merit by a factor of 2-3, depending on the level of systematics. Finally, we present a correction factor necessary for calculating the unbiased Fisher information from finite differences using a limited number of cosmological simulations.

Original language	English
Pages (from-to)	439-446
Number of pages	8
Journal	Monthly Notices of the Royal Astronomical Society
Volume	472
Issue number	1
DOIs	https://doi.org/10.1093/mnras/stx1626
State	Published - Nov 2017

Funding

The work on this paper was supported by Department of Energy grant DE-SC0011726.

Keywords

Cosmological parameters
Cosmology: observations
Gravitational lensing: weak
Large-scale structure of Universe

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10.1093/mnras/stx1626

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title = "Cosmological constraints from the convergence 1-point probability distribution",

abstract = "We examine the cosmological information available from the 1-point probability density function (PDF) of the weak-lensing convergence field, utilizing fast L-PICOLA simulations and a Fisher analysis. We find competitive constraints in the Ωm-σ8 plane from the convergence PDF with 188 arcmin2 pixels compared to the cosmic shear power spectrum with an equivalent number of modes (ℓ < 886). The convergence PDF also partially breaks the degeneracy cosmic shear exhibits in that parameter space. A joint analysis of the convergence PDF and shear 2-point function also reduces the impact of shape measurement systematics, to which the PDF is less susceptible, and improves the total figure of merit by a factor of 2-3, depending on the level of systematics. Finally, we present a correction factor necessary for calculating the unbiased Fisher information from finite differences using a limited number of cosmological simulations.",

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AU - Patton, Kenneth

AU - Blazek, Jonathan

AU - Honscheid, Klaus

AU - Huff, Eric

AU - Melchior, Peter

AU - Ross, Ashley J.

AU - Suchyta, Eric

PY - 2017/11

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AB - We examine the cosmological information available from the 1-point probability density function (PDF) of the weak-lensing convergence field, utilizing fast L-PICOLA simulations and a Fisher analysis. We find competitive constraints in the Ωm-σ8 plane from the convergence PDF with 188 arcmin2 pixels compared to the cosmic shear power spectrum with an equivalent number of modes (ℓ < 886). The convergence PDF also partially breaks the degeneracy cosmic shear exhibits in that parameter space. A joint analysis of the convergence PDF and shear 2-point function also reduces the impact of shape measurement systematics, to which the PDF is less susceptible, and improves the total figure of merit by a factor of 2-3, depending on the level of systematics. Finally, we present a correction factor necessary for calculating the unbiased Fisher information from finite differences using a limited number of cosmological simulations.

KW - Cosmological parameters

KW - Cosmology: observations

KW - Gravitational lensing: weak

KW - Large-scale structure of Universe

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Cosmological constraints from the convergence 1-point probability distribution

Abstract

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Keywords

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