Hyper Suprime-Cam Year 3 Results: Measurements of Clustering of SDSS-BOSS Galaxies, Galaxy-Galaxy Lensing and Cosmic Shear

Kavli Affiliate: Hitoshi Murayama

| First 5 Authors: Surhud More, Sunao Sugiyama, Hironao Miyatake, Markus Michael Rau, Masato Shirasaki

| Summary:

We use the Sloan Digital Sky Survey (SDSS) BOSS galaxies and their overlap
with approximately 416 sq. degree of deep $grizy$-band imaging from the Subaru
Hyper Suprime-Cam Survey (HSC). We measure three two-point correlations that
form the basis of the cosmological inference presented in our companion papers,
Miyatake et al. and Sugiyama et al. We use three approximately volume limited
subsamples of spectroscopic galaxies by their $i$-band magnitude from the
SDSS-BOSS: LOWZ (0.1<z<0.35), CMASS1 (0.43<z<0.55) and CMASS2 (0.55<z<0.7),
respectively. We present high signal-to-noise ratio measurements of the
projected correlation functions of these galaxies, which is expected to be
proportional to the matter correlation function times the bias of galaxies on
large scales. In order to break the degeneracy between the amplitude of the
matter correlation and the bias of these galaxies, we use the distortions of
the shapes of galaxies in HSC due to weak gravitational lensing, to measure the
galaxy-galaxy lensing signal, which probes the galaxy-matter cross-correlation
of the SDSS-BOSS galaxies. We also measure the cosmic shear correlation
functions from HSC galaxies which is related to the projected matter
correlation function. We demonstrate the robustness of our measurements with a
variety of systematic tests. Our use of a single sample of HSC source galaxies
is crucial to calibrate any residual systematic biases in the inferred
redshifts of our galaxies. We also describe the construction of a suite of
mocks: i) spectroscopic galaxy catalogs which obey the clustering and abundance
of each of the three SDSS-BOSS subsamples, and ii) galaxy shape catalogs which
obey the footprint of the HSC survey and have been appropriately sheared by the
large-scale structure expected in a $Lambda$-CDM model. We use these mock
catalogs to compute the covariance of each of our observables.

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