Comparing weak lensing peak counts in baryonic correction models to hydrodynamical simulations
arXiv:2201.08320 · doi:10.1093/mnras/stac3592
Abstract
Next-generation weak lensing (WL) surveys, such as by the Vera Rubin Observatory's LSST, the Space Telescope, and the space mission, will supply vast amounts of data probing small, highly nonlinear scales. Extracting information from these scales requires higher-order statistics and the controlling of related systematics such as baryonic effects. To account for baryonic effects in cosmological analyses at reduced computational cost, semi-analytic baryonic correction models (BCMs) have been proposed. Here, we study the accuracy of BCMs for WL peak counts, a well studied, simple, and effective higher-order statistic. We compare WL peak counts generated from the full hydrodynamical simulation IllustrisTNG and a baryon-corrected version of the corresponding dark matter-only simulation IllustrisTNG-Dark. We apply galaxy shape noise expected at the depths reached by DES, KiDS, HSC, LSST, , and . We find that peak counts in BCMs are (i) accurate at the percent level for peaks with , (ii) statistically indistinguishable from IllustrisTNG in most current and ongoing surveys, but (iii) insufficient for deep future surveys covering the largest solid angles, such as LSST and . We find that BCMs match individual peaks accurately, but underpredict the amplitude of the highest peaks. We conclude that existing BCMs are a viable substitute for full hydrodynamical simulations in cosmological parameter estimation from beyond-Gaussian statistics for ongoing and future surveys with modest solid angles. For the largest surveys, BCMs need to be refined to provide a more accurate match, especially to the highest peaks.
12 pages, 10 figures
References in corpus (18)
- Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report
- Why your model parameter confidences might be too optimistic -- unbiased estimation of the inverse covariance matrix
- The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations
- CFHTLenS: Cosmological constraints from a combination of cosmic shear two-point and three-point correlations
- Modelling baryonic feedback for survey cosmology
- Constraints on the Alignment of Galaxies in Galaxy Clusters from 14,000 Spectroscopic Members
- Cosmic Shear Cosmology Beyond 2-Point Statistics: A Combined Peak Count and Correlation Function Analysis of DES-Y1
- Constraining neutrino mass with weak lensing Minkowski Functionals
- Impact of Baryonic Processes on Weak Lensing Cosmology: Power Spectrum, Non-Local Statistics, and Parameter Bias
- Simultaneous modelling of matter power spectrum and bispectrum in the presence of baryons
- Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian Deep Learning
- The integrated 3-point correlation function of cosmic shear
- TNG: Effect of Baryonic Processes on Weak Lensing with IllustrisTNG Simulations
- Probing dark energy with tomographic weak-lensing aperture mass statistics
- Simultaneously constraining cosmology and baryonic physics via deep learning from weak lensing
- The Impact of Baryons on Cosmological Inference from Weak Lensing Statistics
- Cosmological Constraints from Weak Lensing Peaks: Can Halo Models Accurately Predict Peak Counts?
- Dark Energy Survey Year 3 results: cosmology with moments of weak lensing mass maps
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- Map-level baryonification: unified treatment of weak lensing two-point and higher-order statistics
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