Tomographic galaxy clustering with the Subaru Hyper Suprime-Cam first year public data release

Abstract

We analyze the clustering of galaxies in the first public data release of the Hyper Suprime-Cam Subaru Strategic Program. Despite the relatively small footprints of the observed fields, the data are an excellent proxy for the very deep photometric datasets that will be acquired by the Large Synoptic Survey Telescope, and are therefore an ideal test bed for the analysis methods being implemented by the LSST Dark Energy Science Collaboration. We select a magnitude limited sample with i < 24.5 and analyze it in four tomographic redshift bins covering the range 0.15 z 1.5. We carry out a Fourier-space analysis of the two-point clustering of this sample, including all auto-and cross-correlations between bins. We demonstrate the use of map-level deprojection methods to account for non-physical fluctuations in the galaxy number density caused by observational systematics. Through a halo occupation distribution analysis, we place constraints on the characteristic halo masses of this sample as a function of redshift, finding a good fit up to scales k max = 1 Mpc −1 , including both auto-and cross-correlations. Our results show monotonically decreasing average halo masses with increasing redshift, which can be interpreted in terms of the drop-out of red galaxies at high redshifts for a flux-limited sample, consistent with previous analyses. In terms of photometric redshift systematics, we show that additional care is needed in order to marginalize over uncertainties in the redshift distribution in galaxy clustering, even for samples of this small size, and that these uncertainties can be significantly constrained by including cross-bin correlations. We are able to make a ∼ 3σ detection of the effects of lensing magnification in the HSC data. Our results are stable to variations in the amplitude of density fluctuations σ 8 and the cold dark matter abundance Ω c and we find constraints that agree well with measurements from Planck and low-redshift probes. Finally, we use our analysis pipeline to study the clustering of galaxies as a function of limiting flux, and provide a simple fitting function for the linear galaxy bias for magnitude limited samples as a function of limiting magnitude and redshift. Discussion and Conclusions 50A Reconstructing depth from discrete sources 54

Probing galaxy bias and intergalactic gas pressure with KiDS Galaxies-tSZ-CMB lensing cross-correlations

“…The non-Gaussian term includes a connected contribution resulting from the small-scale non-linear clustering of the tracers, related to the trispectrum of the tracers. According to Koukoufilippas et al (2020); Barreira et al (2018); Nicola et al (2020), this contribution is only significant for low redshifts z 0.2, therefore we neglect it in our covariance matrix. Another non-Gaussian contribution is the super-sample covariance (Takada & Hu 2013, SSC) resulting from mode mixing between observed in-survey and the unobserved out-of-survey modes, we also ignore this in this work.…”

Perturbation theory models for LSST-era galaxy clustering: tests with sub-percent mock catalog measurements in Fourier and configuration space

“…with b 1 (z = 0) = 0.95 and 1.05 for m i < 25.3 and m i < 24.1, implying b 1 (z = 1.0) = 1.51 and 1.68 for those two galaxy samples. Analysis of HSC galaxy clustering in Nicola et al [60] found bias values lower than those provided by the SRD, with b 1 (z = 1.0) ∼ 1.60 and ∼ 1.55 for m i < 23.5 and m i < 24.5. Our analysis of CosmoDC2 galaxies gives b 1 values about 10% lower than the HSC measurements.…”

“…FIG.12. Comparison of our linear bias parameter estimates with observations of HSC galaxy clustering[60], as well as the LSST DESC SRD[61].…”

Dark Energy Survey Year 3 Results: Galaxy mock catalogs for BAO analysis

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