We present cosmological constraints from the Subaru Hyper SuprimeCam (HSC) firstyear weak lensing shear catalogue using convolutional neural networks (CNNs) and conventional summary statistics. We crop 19 $3\times 3\, \mathrm{{deg}^2}$ subfields from the firstyear area, divide the galaxies with redshift 0.3 ≤ z ≤ 1.5 into four equally spaced redshift bins, and perform tomographic analyses. We develop a pipeline to generate simulated convergence maps from cosmological Nbody simulations, where we account for effects such as intrinsic alignments (IAs), baryons, photometric redshift errors, and point spread function errors, to match characteristics of the real catalogue. We train CNNs that can predict the underlying parameters from the simulated maps, and we use them to construct likelihood functions for Bayesian analyses. In the Λ cold dark matter model with two free cosmological parameters Ωm and σ8, we find $\Omega _\mathrm{m}=0.278_{0.035}^{+0.037}$, $S_8\equiv (\Omega _\mathrm{m}/0.3)^{0.5}\sigma _{8}=0.793_{0.018}^{+0.017}$, and the IA amplitude $A_\mathrm{IA}=0.20_{0.58}^{+0.55}$. In a model with four additional free baryonic parameters, we find $\Omega _\mathrm{m}=0.268_{0.036}^{+0.040}$, $S_8=0.819_{0.024}^{+0.034}$, and $A_\mathrm{IA}=0.16_{0.58}^{+0.59}$, with the baryonic parameters not being wellconstrained. We also find that statistical uncertainties of the parameters by the CNNs are smaller than those from the power spectrum (5–24 per cent smaller for S8 and a factor of 2.5–3.0 smallermore »
 Award ID(s):
 1909193
 Publication Date:
 NSFPAR ID:
 10185284
 Journal Name:
 Monthly Notices of the Royal Astronomical Society
 Volume:
 488
 Issue:
 2
 Page Range or eLocationID:
 1652 to 1678
 ISSN:
 00358711
 Sponsoring Org:
 National Science Foundation
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