  As weak lensing surveys go deeper, there is an increasing need for reliable
characterization of non-Gaussian structures at small angular scales. Here we
present the first cosmological constraints with weak lensing scattering
transform, a statistical estimator that combines efficiency, robustness, and
interpretability. With the Hyper Suprime-Cam survey (HSC) year 1 data, we
obtain $\Omega_\text{m}=0.29_{-0.03}^{+0.04}$, $S_8\equiv
\sigma_8(\Omega_\text{m}/0.3)^{0.5}=0.83\pm0.02$, and intrinsic alignment
strength $A_\text{IA}=1.0\pm0.4$ through simulation-based forward modeling. Our
constraints are consistent with those derived from Planck. The error bar of
$\Omega_\text{m}$ is 2 times tighter than that obtained from the power spectrum
when the same scale range is used. This constraining power is on par with that
of convolutional neural networks, suggesting that further investment in spatial
information extraction may not yield substantial benefits.
  We also point out an internal tension of $S_8$ estimates linked to a redshift
bin around z ~ 1 in the HSC data. We found that discarding that bin leads to a
consistent decrease of $S_8$ from 0.83 to 0.79, for all statistical estimators.
We argue that photometric redshift estimation is now the main limitation in the
estimation of $S_8$ using HSC. This limitation is likely to affect other
ground-based weak lensing surveys reaching redshifts greater than one.
Alternative redshift estimation techniques, like clustering redshifts, may help
alleviate this limitation.
