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The HectoMAP survey provides a complete, mass-limited sample of 30,231 quiescent galaxies withi-band Hyper Suprime-Cam Subaru Strategic Program (HSC SSP) imaging that spans the redshift range 0.2 <z< 0.6. We combine half-light radii based on HSC SSP imaging with redshifts andD_n4000 to explore the size–mass relation,$R_e=A\times M_{*}^{\alpha }$, and its evolution for the entire HectoMAP quiescent population and for two subsets of the data. Newcomers with 1.5 <D_n4000 < 1.6 at each redshift show a steeper increase inAas the universe ages than the population that descends from galaxies that are already quiescent at the survey limit,z∼ 0.6 (the resident population). In broad agreement with previous studies, evolution in the size–mass relation both for the entire HectoMAP sample and for the resident population (but not for the newcomers alone) is consistent with minor merger driven growth. For the resident population, the evolution in the size–mass relation is independent of the population age atz∼ 0.6. The contrast between the sample of newcomers and the resident population provides insight into the role of commonly termed “progenitor bias” on the evolution of the size–mass relation.

 

Abstract We explore the redshift evolution of the dynamical properties of massive clusters and their brightest cluster galaxies (BCGs) at z < 2 based on the IllustrisTNG-300 simulation. We select 270 massive clusters with M 200 < 10 14 M ⊙ at z = 0 and trace their progenitors based on merger trees. From 67 redshift snapshots covering z < 2, we compute the 3D subhalo velocity dispersion as a cluster velocity dispersion ( σ cl ). We also calculate the 3D stellar velocity dispersion of the BCGs ( σ *,BCG ). Both σ cl and σ *,BCG increase as the universe ages. The BCG velocity dispersion grows more slowly than the cluster velocity dispersion. Furthermore, the redshift evolution of the BCG velocity dispersion shows dramatic changes at some redshifts resulting from dynamical interaction with neighboring galaxies (major mergers). We show that σ *,BCG is comparable with σ cl at z > 1, offering an interesting observational test. The simulated redshift evolution of σ cl and σ *,BCG generally agrees with an observed cluster sample for z < 0.3, but with large scatter. Future large spectroscopic surveys reaching to high redshift will test the implications of the simulations for the mass evolution of both clusters and their BCGs. 

Abstract We use IllustrisTNG simulations to explore the dynamic scaling relation between massive clusters and their—central—brightest cluster galaxies (BCGs). The IllustrisTNG-300 simulation we use includes 280 massive clusters from the z = 0 snapshot with M 200 > 10 14 M ⊙ , enabling a robust statistical analysis. We derive the line-of-sight velocity dispersion of the stellar particles of the BCGs ( σ *,BCG ), analogous to the observed BCG stellar velocity dispersion. We also compute the subhalo velocity dispersion to measure the cluster velocity dispersion ( σ cl ). Both σ *,BCG and σ cl are proportional to the cluster halo mass, but the slopes differ slightly. Thus, like the observed relation, σ *,BCG / σ cl declines as a function of σ cl , but the scatter is large. We explore the redshift evolution of the σ *,BCG − σ cl scaling relation for z ≲ 1 in a way that can be compared directly with observations. The scaling relation has a similar slope at high redshift, but the scatter increases because of the large scatter in σ *,BCG . The simulations imply that high-redshift BCGs are dynamically more complex than their low-redshift counterparts. 

We use surveys covering the redshift range 0.05 <z< 3.8 to explore quiescent galaxy scaling relations and the redshift evolution of the velocity dispersion, size, and dynamical mass at fixed stellar mass. For redshiftz< 0.6, we derive mass-limited samples and demonstrate that these large samples enhance constraints on the evolution of the quiescent population. The constraints include 2985 new velocity dispersions from the SHELS F2 survey. In contrast with the known substantial evolution of size with redshift, evolution in the velocity dispersion is negligible. The dynamical-to-stellar-mass ratio increases significantly as the universe ages, in agreement with recent results that combine high-redshift data with the Sloan Digital Sky Survey. Like other investigators, we interpret this result as an indication that the dark matter fraction within the effective radius increases as a result of the impact of the minor mergers that are responsible for size growth. We emphasize that dense redshift surveys covering the range 0.07 <z< 1 along with strong and weak lensing measurements could remove many ambiguities in evolutionary studies of the quiescent population.

 

We present the Local Volume Complete Cluster Survey (LoVoCCS; we pronounce it as “low-vox” or “law-vox,” with stress on the second syllable), an NSF’s National Optical-Infrared Astronomy Research Laboratory survey program that uses the Dark Energy Camera to map the dark matter distribution and galaxy population in 107 nearby (0.03 <z< 0.12) X-ray luminous ([0.1–2.4 keV]L_X500> 10⁴⁴erg s⁻¹) galaxy clusters that are not obscured by the Milky Way. The survey will reach Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Year 1–2 depth (for galaxiesr= 24.5,i= 24.0, signal-to-noise ratio (S/N) > 20;u= 24.7,g= 25.3,z= 23.8, S/N > 10) and conclude in ∼2023 (coincident with the beginning of LSST science operations), and will serve as a zeroth-year template for LSST transient studies. We process the data using the LSST Science Pipelines that include state-of-the-art algorithms and analyze the results using our own pipelines, and therefore the catalogs and analysis tools will be compatible with the LSST. We demonstrate the use and performance of our pipeline using three X-ray luminous and observation-time complete LoVoCCS clusters: A3911, A3921, and A85. A3911 and A3921 have not been well studied previously by weak lensing, and we obtain similar lensing analysis results for A85 to previous studies. (We mainly use A3911 to show our pipeline and give more examples in the Appendix.)