Tomographic galaxy clustering with the Subaru Hyper Suprime-Cam first year public data release
Andrina Nicola (Princeton), David Alonso (Oxford), Javier S\'anchez, (Fermilab), An\v{z}e Slosar (BNL), Humna Awan (Rutgers), Adam Broussard, (Rutgers), Jo Dunkley (Princeton), Zahra Gomes (Oxford), Eric Gawiser, (Rutgers), Rachel Mandelbaum (CMU), Hironao Miyatake (Nagoya)

TL;DR
This paper analyzes galaxy clustering in Subaru HSC data, demonstrating methods for systematics correction, halo mass constraints, and lensing magnification detection, providing insights relevant for future LSST analyses.
Contribution
It introduces a Fourier-space clustering analysis with systematic corrections and halo modeling on HSC data, serving as a test bed for LSST DESC methodologies.
Findings
Good fit to halo models up to 1 Mpc^{-1}
Detection of lensing magnification at ~3σ significance
Constraints consistent with Planck and low-redshift data
Abstract
We analyze the clustering of galaxies in the first public data release of the HSC Subaru Strategic Program. Despite the relatively small footprints of the observed fields, the data are an excellent proxy for the deep photometric datasets that will be acquired by LSST, and are therefore an ideal test bed for the analysis methods being implemented by the LSST DESC. We select a magnitude limited sample with and analyze it in four redshift bins covering . We carry out a Fourier-space analysis of the two-point clustering of this sample, including all auto- and cross-correlations. We demonstrate the use of map-level deprojection methods to account for fluctuations in the galaxy number density caused by observational systematics. Through an HOD analysis, we place constraints on the characteristic halo masses of this sample, finding a good fit up to scales…
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Taxonomy
TopicsMedical Imaging Techniques and Applications · Atmospheric and Environmental Gas Dynamics
