Probing dark energy with tomographic weak-lensing aperture mass statistics
Abstract
We forecast and optimize the cosmological power of various weak-lensing aperture mass () map statistics for future cosmic shear surveys, including peaks, voids, and the full distribution of pixels (1D ). These alternative methods probe the non-Gaussian regime of the matter distribution, adding complementary cosmological information to the classical two-point estimators. Based on the SLICS and cosmo-SLICS -body simulations, we build Euclid-like mocks to explore the parameter space. We develop a new tomographic formalism which exploits the cross-information between redshift slices (cross-) in addition to the information from individual slices (auto-) probed in the standard approach. Our auto- forecast precision is in good agreement with the recent literature on weak-lensing peak statistics, and is improved by % when including cross-. It is further boosted by the use of 1D that outperforms all other estimators, including the shear two-point correlation function (-2PCF). When considering all tomographic terms, our uncertainty range on the structure growth parameter is enhanced by % (almost twice better) when combining 1D and the -2PCF compared to the -2PCF alone. We additionally measure the first combined forecasts on the dark energy equation of state , finding a factor of three reduction of the statistical error compared to the -2PCF alone. This demonstrates that the complementary cosmological information explored by non-Gaussian map statistics not only offers the potential to improve the constraints on the recent - tension, but also constitutes an avenue to understand the accelerated expansion of our Universe.
Keywords
Cite
@article{arxiv.2010.07376,
title = {Probing dark energy with tomographic weak-lensing aperture mass statistics},
author = {Nicolas Martinet and Joachim Harnois-Déraps and Eric Jullo and Peter Schneider},
journal= {arXiv preprint arXiv:2010.07376},
year = {2021}
}
Comments
Matching version accepted by A&A, 18 pages, 12 figures