Analytical marginalisation over photometric redshift uncertainties in cosmic shear analyses

Abstract

As the statistical power of imaging surveys grows, it is crucial to account for all systematic uncertainties. This is normally done by constructing a model of these uncertainties and then marginalizing over the additional model parameters. The resulting high dimensionality of the total parameter spaces makes inferring the cosmological parameters significantly more costly using traditional Monte-Carlo sampling methods. A particularly relevant example is the redshift distribution, $p(z)$, of the source samples, which may require tens of parameters to describe fully. However, relatively tight priors can be usually placed on these parameters through calibration of the associated systematics. In this paper we show, quantitatively, that a linearisation of the theoretical prediction with respect to these calibratable systematic parameters allows us to analytically marginalise over these extra parameters, leading to a factor $\sim 30$ reduction in the time needed for parameter inference, while accurately recovering the same posterior distributions for the cosmological parameters that would be obtained through a full numerical marginalisation over 160 $p(z)$ parameters. We demonstrate that this is feasible not only with current data and current achievable calibration priors but also for future Stage-IV datasets.

11 pages, 8 figures, prepared for submission to MNRAS, comments welcome

References in corpus (5)

• The NumPy array: a structure for efficient numerical computation
• Calibrating Redshift Distributions Beyond Spectroscopic Limits with Cross-Correlations
• Estimating the Redshift Distribution of Faint Galaxy Samples
• Using Galaxy Two-point Correlation Functions to Determine the Redshift Distributions of Galaxies Binned by Photometric Redshift
• Reconstructing Redshift Distributions with Cross-Correlations: Tests and an Optimized Recipe