Hyper Suprime-Cam Year 3 Results: Cosmology from Cosmic Shear Power Spectra

TL;DR

This work presents a high-precision tomographic cosmic shear power-spectrum analysis from the Hyper Suprime-Cam Year 3 data, measuring EE/BB/EB spectra with 416 deg^2 and ~15 arcmin^{-2} effective galaxy density. Using a Pseudo-Cℓ approach and NaMaster, it rigorously accounts for survey masks, covariance from 1404 realistic mocks, and a suite of astrophysical and observational systematics, including baryonic feedback and intrinsic alignments. The fiducial flat ΛCDM analysis yields S8 = 0.776^{+0.032}_{-0.033}, with results robust to modeling choices and internal consistency checks, though mildly in tension with Planck 2018 by about 2σ. The paper also discusses residual photo-z biases, prior choices, and the path forward with future surveys to refine S8 and resolve tensions observed across probes.

Abstract

We measure weak lensing cosmic shear power spectra from the three-year galaxy shear catalog of the Hyper Suprime-Cam (HSC) Subaru Strategic Program imaging survey. The shear catalog covers $416 \ \mathrm{deg}^2$ of the northern sky, with a mean $i$-band seeing of 0.59 arcsec and an effective galaxy number density of 15 $\mathrm{arcmin}^{-2}$ within our adopted redshift range. With an $i$-band magnitude limit of 24.5 mag, and four tomographic redshift bins spanning $0.3 \leq z_{\mathrm{ph}} \leq 1.5$ based on photometric redshifts, we obtain a high-significance measurement of the cosmic shear power spectra, with a signal-to-noise ratio of approximately 26.4 in the multipole range $300<\ell<1800$. The accuracy of our power spectrum measurement is tested against realistic mock shear catalogs, and we use these catalogs to get a reliable measurement of the covariance of the power spectrum measurements. We use a robust blinding procedure to avoid confirmation bias, and model various uncertainties and sources of bias in our analysis, including point spread function systematics, redshift distribution uncertainties, the intrinsic alignment of galaxies and the modeling of the matter power spectrum. For a flat $Λ$CDM model, we find $S_8 \equiv σ_8 (Ω_m/0.3)^{0.5} =0.776^{+0.032}_{-0.033}$, which is in excellent agreement with the constraints from the other HSC Year 3 cosmology analyses, as well as those from a number of other cosmic shear experiments. This result implies a $\sim$$2σ$-level tension with the Planck 2018 cosmology. We study the effect that various systematic errors and modeling choices could have on this value, and find that they can shift the best-fit value of $S_8$ by no more than $\sim$$0.5σ$, indicating that our result is robust to such systematics.

Figures (18)

• Figure 1: Tomographic cosmic shear power spectra of $EE$ (blue circle), $BB$ (orange triangle), and $EB$ (green cross) modes. The galaxy sample is divided into four tomographic bins, with redshift ranges $(0.3, 0.6]$, $(0.6, 0.9]$, $(0.9, 1.2]$ and $(1.2, 1.5]$, using the "best" photo-$z$ estimation by the DNNz algorithm. These bins are referred to as bin numbers 1 to 4 respectively. The scales $\ell<300$ and $\ell>1800$ (shaded regions) are excluded in the cosmological analysis, based on the scale cuts described in Sections \ref{['sec:null_tests']}, \ref{['sec:model_sufficiency']}, and \ref{['sec:ia']}. The combined total signal-to-noise ratio of the $EE$ spectra is 26.4 in the range of our fiducial scale cuts. Both the $BB$ and $EB$ spectra are consistent with zero.
• Figure 2: Normalized covariance matrix (correlation coefficients), measured as described in Section \ref{['sec:covariance']}, for the fiducial scale cuts $300 < \ell < 1800$, and the auto- and cross-correlations of four tomographic redshift bins (10 spectra in total, with 6 bins each in $\ell$).
• Figure 3: Additive bias to the cosmic shear power spectrum from PSF systematics, $\Delta C_{\ell}$ (orange triangles; see Section \ref{['sec:psf_systematics']} for details), compared to the uncertainty of the non-tomographic cosmic shear power spectrum, $\sigma_{C_{\ell}^{EE}}$ (filled circles). The scales $\ell < 300$ and $\ell > 1800$ (shaded regions) are excluded in the cosmological analysis. We find that the contribution of PSF systematics is about $30\%$ of the uncertainty in the power spectra, so we marginalize over these systematics parameters in our cosmological analysis.
• Figure 4: Source redshift distribution inferred from the stacked DNNz photometric redshifts, with a cosmic variance correction (grey contour), the clustering redshifts using the CAMIRA LRG sample (black points), and the joint inference combining the two methods (red contour). The mean of the joint inference contour is used as our fiducial source redshift distribution model. This figure has been adapted from Figure 8 of Rau2022.
• Figure 5: Blinded parameter constraints from different IA models (described in Section \ref{['sec:ia']}) and different scale cuts, run on blinded catalog 0, which has the most constraining power. These results were used to choose the fiducial IA model and small scale cut. Since the constraints on our parameter of interest, $S_8$, are comparable for both NLA and TATT (with no shift in the central value, or appreciable increase in error bars), we choose to use the more complete model, TATT. The constraints on $S_8$ are also similar for the two sets of scale cuts, so we use the more conservative scale cut of $\ell_{\mathrm{max}} = 1800$.