Euclid preparation: XXVIII. Forecasts for ten different higher-order weak lensing statistics
Recent cosmic shear studies have shown that higher-order statistics (HOS) developed by independent teams now outperform standard two-point estimators in terms of statistical precision thanks to their sensitivity to the non-Gaussian features of large-scale structure. The aim of the Higher-Order Weak...
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Veröffentlicht in: | Astronomy and astrophysics (Berlin) 2023-07, Vol.675, p.A120 |
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Sprache: | eng |
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Zusammenfassung: | Recent cosmic shear studies have shown that higher-order statistics (HOS) developed by independent teams now outperform standard two-point estimators in terms of statistical precision thanks to their sensitivity to the non-Gaussian features of large-scale structure. The aim of the Higher-Order Weak Lensing Statistics (HOWLS) project is to assess, compare, and combine the constraining power of ten different HOS on a common set of
Euclid
-like mocks, derived from
N
-body simulations. In this first paper of the HOWLS series, we computed the nontomographic (Ω
m
,
σ
8
) Fisher information for the one-point probability distribution function, peak counts, Minkowski functionals, Betti numbers, persistent homology Betti numbers and heatmap, and scattering transform coefficients, and we compare them to the shear and convergence two-point correlation functions in the absence of any systematic bias. We also include forecasts for three implementations of higher-order moments, but these cannot be robustly interpreted as the Gaussian likelihood assumption breaks down for these statistics. Taken individually, we find that each HOS outperforms the two-point statistics by a factor of around two in the precision of the forecasts with some variations across statistics and cosmological parameters. When combining all the HOS, this increases to a 4.5 times improvement, highlighting the immense potential of HOS for cosmic shear cosmological analyses with
Euclid
. The data used in this analysis are publicly released with the paper. |
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ISSN: | 0004-6361 1432-0746 |
DOI: | 10.1051/0004-6361/202346017 |