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• W4210693024 abstract "Ongoing and planned weak lensing (WL) surveys are becoming deep enough to contain information on angular scales down to a few arcmin. To fully extract information from these small scales, we must capture non-Gaussian features in the cosmological WL signal while accurately accounting for baryonic effects. In this work, we account for baryonic physics via a baryonic correction model that modifies the matter distribution in dark matter-only $N$-body simulations, mimicking the effects of galaxy formation and feedback. We implement this model in a large suite of ray-tracing simulations, spanning a grid of cosmological models in $Omega_mathrm{m}-sigma_8$ space. We then develop a convolutional neural network (CNN) architecture to learn and constrain cosmological and baryonic parameters simultaneously from the simulated WL convergence maps. We find that in a Hyper-Suprime Cam (HSC)-like survey, our CNN achieves a 1.7$times$ tighter constraint in $Omega_mathrm{m}-sigma_8$ space ($1sigma$ area) than the power spectrum and 2.1$times$ tighter than the peak counts, showing that the CNN can efficiently extract non-Gaussian cosmological information even while marginalizing over baryonic effects. When we combine our CNN with the power spectrum, the baryonic effects degrade the constraint in $Omega_mathrm{m}-sigma_8$ space by a factor of 2.4, compared to the much worse degradation by a factor of 4.7 or 3.7 from either method alone." @default.
• W4210693024 created "2022-02-08" @default.
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• W4210693024 date "2022-01-28" @default.
• W4210693024 modified "2023-10-16" @default.
• W4210693024 title "Simultaneously constraining cosmology and baryonic physics via deep learning from weak lensing" @default.
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• W4210693024 doi "https://doi.org/10.1093/mnras/stac161" @default.
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