First results on the cluster galaxy population from the Subaru Hyper Suprime-Cam survey. II. Faint end color–magnitude diagrams and radial profiles of red and blue galaxies at 0.1 < z < 1.1

Abstract

We present a statistical study of the redshift evolution of the cluster galaxy population over a wide redshift range from 0.1 to 1.1, using ∼ 1900 optically-selected CAMIRA clusters from ∼ 232 deg 2 of the Hyper Suprime-Cam (HSC) Wide S16A data. Our stacking technique with a statistical background subtraction reveals color-magnitude diagrams of red-sequence and blue c 2014. Astronomical Society of Japan. 2Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0 cluster galaxies down to faint magnitudes of m z ∼ 24. We find that the linear relation of redsequence galaxies in the color-magnitude diagram extends down to the faintest magnitudes we explore with a small intrinsic scatter σ int (g − r) < 0.1. The scatter does not evolve significantly with redshift. The stacked color-magnitude diagrams are used to define red and blue galaxies in clusters for studying their radial number density profiles without resorting to photometric redshifts of individual galaxies. We find that red galaxies are significantly more concentrated toward cluster centers and blue galaxies dominate the outskirt of clusters. We explore the fraction of red galaxies in clusters as a function of redshift, and find that the red fraction decreases with increasing distances from cluster centers. The red fraction exhibits a moderate decrease with increasing redshift. The radial number density profiles of cluster member galaxies are also used to infer the location of the steepest slope in the three dimensional galaxy density profiles. For a fixed threshold in richness, we find little redshift evolution in this location.

On the "Lensing is Low" of BOSS Galaxies

“…Firstly, the CMASS colour selection includes more galaxies that are in the blue cloud, while the LOWZ selection follows more faithfully the traditional LRG colour cuts (Eisenstein et al 2001). Secondly, galaxies in massive haloes are in general bluer at higher redshifts (Cooper et al 2007;Hansen et al 2009;Nishizawa et al 2018). In addition, Figure 7 shows that this preference becomes even stronger for CMASS galaxies at higher redshifts, bringing relatively more galaxies from the low-mass haloes into the sample.…”

“…They found that the halo mass quenching model provides an excellent fit to the SDSS measurements, while the other two exhibit strong discrepancy between the g-g lensing and clustering of SDSS galaxies at the high-mass end (see also Mandelbaum et al 2016;, in a very similar fashion to the current tension found within BOSS (see their figure 10). In this paper, we build on the iHOD halo quenching framework of Zu & Mandelbaum (2015, 2018 and incorporate the stellar as well as halo mass dependence of galaxy selection functions into our analytic HOD framework, in hopes of reproducing the observed low small-scale g-g lensing signal at fixed large-scale galaxy bias.…”

The mass-size relation of galaxy clusters

“…In the cold dark matter scenario, our result implies that galaxies can be used to trace the edge of clusters. We note, in particular, that this measurement has already been performed several times using photometric surveys (Baxter et al 2017;Nishizawa et al 2017;Chang et al 2018;Zürcher & More 2019;Shin et al 2019). Furthermore, due to the large number of objects detected, galaxy distributions obtained through this method offer the most precise measurements of splashback.…”

A dynamics-based density profile for dark haloes. I. Algorithm and basic results

“…The halo outskirts (r > ∼ 0.5 R vir ) have recently received renewed attention because they turn out to be sensitive to otherwise inaccessible halo properties such as the mass accretion rate (Diemer & Kravtsov 2014, hereafter DK14; see also Xhakaj et al 2020Xhakaj et al , 2021, the nature of dark matter (Banerjee et al 2020), and deviations from General Relativity (Adhikari et al 2018;Contigiani et al 2019a). At the same time, rapid observational progress has led to high-accuracy constraints on profiles from profiles of satellites in individual clusters (Rines et al 2013;Tully 2015;Patej & Loeb 2016), in stacked clusters (More et al 2016;Baxter et al 2017;Nishizawa et al 2018;Zürcher & More 2019;Murata et al 2020), and in weak lensing profiles (Mandelbaum et al 2006;Umetsu et al 2011;Umetsu & Diemer 2017;Contigiani et al 2019b;Chang et al 2018;Shin et al 2019Shin et al , 2021. Deviations from simple fitting functions for the orbiting profile have long been known to bias weak lensing masses (Becker & Kravtsov 2011;Oguri & Hamana 2011), but the recently acquired high signal-to-noise in the outer profiles has made detailed theoretical modelling even more pressing.…”