Magnification effect on the detection of primordial non-Gaussianity from photometric surveys

TL;DR

This paper forecasts how gravitational lensing magnification affects the measurement of primordial non-Gaussianity in photometric surveys, highlighting biases and the importance of cosmic shear data and tomography for accurate constraints.

Contribution

It quantifies the systematic bias caused by magnification in estimating fNL and demonstrates how combining cosmic shear and tomography can mitigate this bias in future surveys.

Findings

Magnification causes significant bias in fNL estimates if ignored.

Cosmic shear data helps reduce bias and improve constraints.

Tomography enhances fNL constraints but can increase systematic bias.

Abstract

We present forecast results for constraining the primordial non-Gaussianity from photometric surveys through a large-scale enhancement of the galaxy clustering amplitude. In photometric surveys, the distribution of observed galaxies at high redshifts suffers from the gravitational-lensing magnification, which systematically alters the number density for magnitude-limited galaxy samples. We estimate size of the systematic bias in the best-fit cosmological parameters caused by the magnification effect, particularly focusing on the primordial non-Gaussianity. For upcoming deep and/or wide photometric surveys like HSC, DES and LSST, the best-fit value of the non-Gaussian parameter, fNL, obtained from the galaxy count data is highly biased, and the true values of fNL would typically go outside the 3-sigma error of the biased confidence region, if we ignore the magnification effect in the theoretical template of angular power spectrum. The additional information from cosmic shear data helps not only to improve the constraint, but also to reduce the systematic bias. As a result, the size of systematic bias on fNL would become small enough compared to the expected 1-sigma error for HSC and DES, but it would be still serious for deep surveys with z_m > 1.5, like LSST. Tomographic technique improves the constraint on fNL by a factor of 2-3 compared to the one without tomography, but the systematic bias would increase.