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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| Main Authors: | , , , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
07 July 2023
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| In: |
Astronomy and astrophysics
Year: 2023, Volume: 675, Pages: 1-32 |
| ISSN: | 1432-0746 |
| DOI: | 10.1051/0004-6361/202346017 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1051/0004-6361/202346017 Verlag, lizenzpflichtig, Volltext: https://www.aanda.org/articles/aa/abs/2023/07/aa46017-23/aa46017-23.html |
| Author Notes: | Euclid collaboration: V. Ajani, M. Baldi, A. Barthelemy, A. Boyle, P. Burger, V.F. Cardone, S. Cheng, S. Codis, C. Giocoli, J. Harnois-Déraps, S. Heydenreich, V. Kansal, M. Kilbinger, L. Linke, C. Llinares, N. Martinet, K. Jahnke, G. Seidel, M. Maturi, Z. Sakr [und 193 weitere] |
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| 520 | |a 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 (Ωm, σ8) 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. ... | ||
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