Cosmology from HSC Y1 Weak Lensing with Combined HigherOrder Statistics and Simulationbased Inference
Abstract
We present cosmological constraints from weak lensing with the Subaru Hyper SuprimeCam (HSC) firstyear (Y1) data, using a simulationbased inference (SBI) method. % We explore the performance of a set of higherorder statistics (HOS) including the Minkowski functionals, counts of peaks and minima, and the probability distribution function and compare them to the traditional twopoint statistics. The HOS, also known as nonGaussian statistics, can extract additional nonGaussian information that is inaccessible to the twopoint statistics. We use a neural network to compress the summary statistics, followed by an SBI approach to infer the posterior distribution of the cosmological parameters. We apply cuts on angular scales and redshift bins to mitigate the impact of systematic effects. Combining twopoint and nonGaussian statistics, we obtain $S_8 \equiv \sigma_8 \sqrt{\Omega_m/0.3} = 0.804_{0.040}^{+0.041}$ and $\Omega_m = 0.344_{0.090}^{+0.083}$, similar to that from nonGaussian statistics alone. These results are consistent with previous HSC analyses and Planck 2018 cosmology. Our constraints from nonGaussian statistics are $\sim 25\%$ tighter in $S_8$ than twopoint statistics, where the main improvement lies in $\Omega_m$, with $\sim 40$\% tighter error bar compared to using the angular power spectrum alone ($S_8 = 0.766_{0.056}^{+0.054}$ and $\Omega_m = 0.365_{0.141}^{+0.148}$). We find that, among the nonGaussian statistics we studied, the Minkowski functionals are the primary driver for this improvement. Our analyses confirm the SBI as a powerful approach for cosmological constraints, avoiding any assumptions about the functional form of the data's likelihood.
 Publication:

arXiv eprints
 Pub Date:
 September 2024
 DOI:
 10.48550/arXiv.2409.01301
 arXiv:
 arXiv:2409.01301
 Bibcode:
 2024arXiv240901301N
 Keywords:

 Astrophysics  Cosmology and Nongalactic Astrophysics
 EPrint:
 14 pages, 7 figures