We measure weak lensing cosmic shear energy spectra from the three-yr galaxy shear catalog of the Hyper Suprime-Cam (HSC) Subaru Strategic Program imaging survey. 1800 attributable to theoretical uncertainties in our modeling of those nonlinear scales (based mostly on exams described in Sections V.2.1 and V.3). This provides us six multipole bins that we use in our fiducial analysis, from the 17 that we originally measure. We derive the covariance matrix of our cosmic shear power spectra utilizing the mock catalogs described in Section II.5. We accomplish that by measuring the cosmic shear Wood Ranger Power Shears shop spectra, following the same method as our measurement from information, from all 1404 realizations of the mocks, and then using these 1404 energy spectra to compute the covariance matrix. Gaussian and non-Gaussian components. We treat these mock catalogs exactly like our real data. We first measure the facility spectra from each mock catalog on a field-by-area foundation.

We analytically compute the additive noise bias from form noise, and subtract it from the measured energy spectra. Finally, we co-add the spectra from each subject for each mock realization utilizing inverse variance weighting, with the person covariances measured from the mock power spectra for each of the fields. We then use the 1404 co-added spectra to compute the covariance matrix. Figure 2 exhibits the correlation matrix from this covariance matrix. For this reason, our covariance estimation needs to be applicable for this evaluation. This correction factor is computed for every redshift bin (1.14, wood shears 0.98, 0.95 and 0.Ninety nine in order of accelerating redshift), and it's roughly fixed throughout the range of scales thought of. 10,000. This restrict is much smaller than the scales thought-about on this analysis. The likelihood, described in Section V.1, makes use of the inverse of the covariance matrix. 0.96, the place 1404 is the number of mock realizations and 60 is the number of knowledge points used within the analysis. This correction assumes Gaussian noise and statistically unbiased data vectors. To first order, we do not anticipate to see any cosmological B