Masked areas in shear peak statistics: a forward modeling approach

TL;DR

This paper presents a forward-modeling approach to account for masked areas in shear peak statistics, improving cosmological parameter estimation accuracy in large surveys like LSST and DES.

Contribution

It introduces a method to incorporate survey masks into theoretical predictions, reducing uncertainties and biases in cosmological constraints from shear maps.

Findings

Masking reduces statistical uncertainties by up to 14%.

Bias in parameter estimation is about 1%, mainly from simulation volume limits.

Reconstructed aperture mass maps smooth out masking effects.

Abstract

The statistics of shear peaks have been shown to provide valuable cosmological information beyond the power spectrum, and will be an important constraint of models of cosmology with the large survey areas provided by forthcoming astronomical surveys. Surveys include masked areas due to bright stars, bad pixels etc, which must be accounted for in producing constraints on cosmology from shear maps. We advocate a forward-modeling approach, where the impact of masking (and other survey artifacts) are accounted for in the theoretical prediction of cosmological parameters, rather than removed from survey data. We use masks based on the Deep Lens Survey, and explore the impact of up to 37% of the survey area being masked on LSST and DES-scale surveys. By reconstructing maps of aperture mass, the masking effect is smoothed out, resulting in up to 14% smaller statistical uncertainties compared to simply reducing the survey area by the masked area. We show that, even in the presence of large survey masks, the bias in cosmological parameter estimation produced in the forward-modeling process is ~1%, dominated by bias caused by limited simulation volume. We also explore how this potential bias scales with survey area and find that small survey areas are more significantly impacted by the differences in cosmological structure in the data and simulated volumes, due to cosmic variance.