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The newly updated \texttt{GIZMO} and \texttt{Simba} based simulation, \texttt{Simba-C}, with its new stellar feedback, chemical enrichment, and recalibrated AGN feedback, allows for a detailed study of the intragroup medium X-ray properties. We discuss the impact of various physical mechanisms, e.g. stellar and AGN feedback, and chemical enrichment, on the composition and the global scaling relations of nearby galaxy groups. We also study the evolution ($z=2$ to $0$) of the global properties for the $1\,\mathrm{keV}$ temperature groups. \texttt{Simba-C} shows improved consistent matching with the observations of all X-ray scaling relations compared to \texttt{Simba}. It is well known that AGN feedback has a significant influence on $L_{X,0.5-2.0}-T_{spec,corr}$, $S_{500/2500}-T_{spec,corr}$, and gas mass fractions, with our \texttt{Simba-C} results consistent with it. Our recalibrated AGN feedback strength also showed an additional improvement in gas entropy, which now aligns with CLoGS observations. The updated stellar feedback and chemical enrichment model is shown to play an important role in our understanding of the chemical abundance ratios and their evolution within galaxy groups. In particular, we find that \texttt{Simba-C} produces an increase in the amount of heavier elements (specifically Si and Fe) relative to O, compared to \texttt{Simba}., Comment: 20 pages, 13 figures, 2 tables, accepted by MNRAS on 6 June 2024

Using high-resolution {\sc Romulus} simulations, we explore the origin and evolution of the circumgalactic medium (CGM) in the region 0.1 $\leq \mathrm{R}/\mathrm{R}_\mathrm{500} \leq$ 1 around massive central galaxies in group-scale halos. We find that the CGM is multiphase and highly dynamic. Investigating the dynamics, we identify seven patterns of evolution. We show that these are robust and detected consistently across various conditions. The gas cools via two pathways: (1) filamentary cooling inflows and (2) condensations forming from rapidly cooling density perturbations. In our cosmological simulations, the perturbations are mainly seeded by orbiting substructures. The condensations can form even when the median $t_\mathrm{cool} / t_\mathrm{ff}$ of the X-ray emitting gas is above 10 or 20. Strong amplitude perturbations can provoke runaway cooling regardless of the state of the background gas. We also find perturbations whose local $t_\mathrm{cool} / t_\mathrm{ff}$ ratios drop below the threshold but which do not condense. Rather, the ratios fall to some minimum value and then bounce. These are weak perturbations that are temporarily swept up in satellite wakes and carried to larger radii. Their $t_\mathrm{cool} / t_\mathrm{ff}$ ratios decrease because $t_\mathrm{ff}$ is increasing, not because $t_\mathrm{cool}$ is decreasing. For structures forming hierarchically, our study highlights the challenge of using a simple threshold argument to infer the CGM's evolution. It also highlights that the median hot gas properties are suboptimal determinants of the CGM's state and dynamics. Realistic CGM models must incorporate the impact of mergers and orbiting satellites, along with the CGM's heating and cooling cycles., Comment: 25 pages, 12 figures

Recently, several studies reported a significant discrepancy between the clustering and lensing of the Baryon Oscillation Spectroscopic Survey (BOSS) galaxies in the $\textit{Planck}$ cosmology. We construct a simple yet powerful model based on the linear theory to assess whether this discrepancy points toward deviations from $\textit{Planck}$. Focusing on scales $10

The bright central galaxies (BCGs) dominate the inner portion of the diffuse cluster light, but it is still unclear where the intracluster light (ICL) takes over. To investigate the BCG-ICL transition, we stack the images of ${\sim}3000$ clusters between $0.2{<}z{<}0.3$ in the SDSS $gri$ bands, and measure their BCG+ICL stellar surface mass profile $\Sigma_{*}^{\texttt{B+I}}$ down to $3{\times}10^4\,M_{\odot}/\mathrm{kpc}^{2}$ at $R{\simeq}1\,\mathrm{Mpc}$ (${\sim}32$ mag/arcsec$^2$ in the $r$-band). We develop a physically-motivated method to decompose $\Sigma_{*}^{\texttt{B+I}}$ into three components, including an inner de Vaucouleurs' profile, an outer ICL that follows the dark matter distribution measured from weak lensing, and an intriguing transitional component between 70 and 200 kpc. To investigate the origin of this transition, we split the clusters into two subsamples by their BCG stellar mass $M_*^{\mathrm{BCG}}$ (mass enclosed roughly within 50 kpc) while making sure they have the same distribution of satellite richness. The $\Sigma_{*}^{\texttt{B+I}}$ profiles of the two subsamples differ by more than a factor of two at $R{<}50\,\mathrm{kpc}$, consistent with their 0.34 dex difference in $M_*^{\mathrm{BCG}}$, whereas on scales beyond 400 kpc the two profiles converge to the same amplitudes, suggesting a satellite-stripping origin of the outer ICL. Remarkably, however, the discrepancy between the two $\Sigma_{*}^{\texttt{B+I}}$ profiles persist at above $50\%$ level on all scales below 200 kpc, thereby revealing the BCG sphere of influence with radius $R_{\mathrm{SOI}}{\simeq}$ 200 kpc. Finally, we speculate that the surprisingly large sphere of influence of the BCG is tied to the elevated escape velocity profile within $r_s$, the characteristic radius of the dark matter haloes., Comment: 16 pages, 13 figures; Matched to the version accepted by MNRAS

Understanding the physical connection between cluster galaxies and massive haloes is key to mitigating systematic uncertainties in next-generation cluster cosmology. We develop a novel method to infer the level of conformity between the stellar mass of the brightest central galaxies~(BCGs) $M_*^{BCG}$ and the satellite richness $\lambda$, defined as their correlation coefficient $\rho_{cc}$ at fixed halo mass, using the abundance and weak lensing of SDSS clusters as functions of $M_*^{BCG}$ and $\lambda$. We detect a halo mass-dependent conformity as $\rho_{cc}{=}0.60{+}0.08\ln(M_h/3{\times}10^{14}M_{\odot}/h)$. The strong conformity successfully resolves the "halo mass equality" conundrum discovered in Zu et al. 2021 -- when split by $M_*^{BCG}$ at fixed $\lambda$, the low and high-$M_*^{BCG}$ clusters have the same average halo mass despite having a $0.34$ dex discrepancy in average $M_*^{BCG}$. On top of the best-fitting conformity model, we develop a cluster assembly bias~(AB) prescription calibrated against the CosmicGrowth simulation, and build a conformity+AB model for the cluster weak lensing measurements. Our model predicts that with a ${\sim}20\%$ lower halo concentration $c$, the low-$M_*^{BCG}$ clusters are ${\sim}10\%$ more biased than the high-$M_*^{BCG}$ systems, in excellent agreement with the observations. We also show that the observed conformity and assembly bias are unlikely due to projection effects. Finally, we build a toy model to argue that while the early-time BCG-halo co-evolution drives the $M_*^{BCG}$-$c$ correlation, the late-time dry merger-induced BCG growth naturally produces the $M_*^{BCG}$-$\lambda$ conformity despite the well-known anti-correlation between $\lambda$ and $c$. Our method paves the path towards simultaneously constraining cosmology and cluster formation with future cluster surveys., Comment: Final MN accepted version (w/ Fig 6 replaced)

Concentration is one of the key dark matter halo properties that could drive the scatter in the stellar-to-halo mass relation of massive clusters. We derive robust photometric stellar masses for a sample of brightest central galaxies (BCGs) in SDSS redMaPPer clusters at $0.17

3D object detection from raw and sparse point clouds has been far less treated to date, compared with its 2D counterpart. In this paper, we propose a novel framework called FVNet for 3D front-view proposal generation and object detection from point clouds. It consists of two stages: generation of front-view proposals and estimation of 3D bounding box parameters. Instead of generating proposals from camera images or bird's-eye-view maps, we first project point clouds onto a cylindrical surface to generate front-view feature maps which retains rich information. We then introduce a proposal generation network to predict 3D region proposals from the generated maps and further extrude objects of interest from the whole point cloud. Finally, we present another network to extract the point-wise features from the extruded object points and regress the final 3D bounding box parameters in the canonical coordinates. Our framework achieves real-time performance with 12ms per point cloud sample. Extensive experiments on the 3D detection benchmark KITTI show that the proposed architecture outperforms state-of-the-art techniques which take either camera images or point clouds as input, in terms of accuracy and inference time., Comment: 10 pages, 6 figures