ABSTRACT
In a purely cold dark matter (CDM) universe, the initial matter power spectrum and its subsequent gravitational growth contain no special mass- or time-scales, and so neither do the emergent ...population statistics of internal dark matter (DM) halo properties. Using 1.5 million haloes from three illustristng realizations of a ΛCDM universe, we show that galaxy formation physics drives non-monotonic features (‘wiggles’) into DM property statistics across six decades in halo mass, from dwarf galaxies to galaxy clusters. We characterize these features by extracting the halo mass-dependent statistics of five DM halo properties – velocity dispersion, NFW concentration, density- and velocity-space shapes, and formation time – using kernel-localized linear regression (Kllr). Comparing precise estimates of normalizations, slopes, and covariances between realizations with and without galaxy formation, we find systematic deviations across all mass-scales, with maximum deviations of 25 per cent at the Milky Way mass of $10^{12} \, {\rm M}_\odot$. The mass-dependence of the wiggles is set by the interplay between different cooling and feedback mechanisms, and we discuss its observational implications. The property covariances depend strongly on halo mass and physics treatment, but the correlations are mostly robust. Using multivariate Kllr and interpretable machine learning, we show the halo concentration and velocity-space shape are principal contributors, at different mass, to the velocity dispersion variance. Statistics of mass accretion rate and DM surface pressure energy are provided in an appendix. We publicly release halo property catalogues and kllr parameters for the TNG runs at 20 epochs up to z = 12.
We use simulated galaxy surveys to study: (i) how galaxy membership in redMaPPer clusters maps to the underlying halo population, and (ii) the accuracy of a mean dynamical cluster mass, M
σ(λ), ...derived from stacked pairwise spectroscopy of clusters with richness λ. Using ∼130 000 galaxy pairs patterned after the Sloan Digital Sky Survey (SDSS) redMaPPer cluster sample study of Rozo et al., we show that the pairwise velocity probability density function of central–satellite pairs with m
i
< 19 in the simulation matches the form seen in Rozo et al. Through joint membership matching, we deconstruct the main Gaussian velocity component into its halo contributions, finding that the top-ranked halo contributes ∼60 per cent of the stacked signal. The halo mass scale inferred by applying the virial scaling of Evrard et al. to the velocity normalization matches, to within a few per cent, the log-mean halo mass derived through galaxy membership matching. We apply this approach, along with miscentring and galaxy velocity bias corrections, to estimate the log-mean matched halo mass at z = 0.2 of SDSS redMaPPer clusters. Employing the velocity bias constraints of Guo et al., we find 〈ln (M
200c)|λ〉 = ln (M
30) + αm ln (λ/30) with M
30 = 1.56 ± 0.35 × 1014 M⊙ and αm = 1.31 ± 0.06stat ± 0.13sys. Systematic uncertainty in the velocity bias of satellite galaxies overwhelmingly dominates the error budget.
Abstract
The underlying physics of astronomical systems govern the relation between their measurable properties. Consequently, quantifying the statistical relationships between system-level ...observable properties of a population offers insights into the astrophysical drivers of that class of systems. While purely linear models capture behavior over a limited range of system scale, the fact that astrophysics is ultimately scale dependent implies the need for a more flexible approach to describing population statistics over a wide dynamic range. For such applications, we introduce and implement a class of kernel localized linear regression
(KLLR)
models.
KLLR
is a natural extension to the commonly used linear models that allows the parameters of the linear model—normalization, slope, and covariance matrix—to be scale dependent.
KLLR
performs inference in two steps: (1) it estimates the mean relation between a set of independent variables and a dependent variable and; (2) it estimates the conditional covariance of the dependent variables given a set of independent variables. We demonstrate the model's performance in a simulated setting and showcase an application of the proposed model in analyzing the baryonic content of dark matter halos. As a part of this work, we publicly release a Python implementation of the
KLLR
method.
ABSTRACT
We study stellar property statistics, including satellite galaxy occupation, of haloes in three cosmological hydrodynamics simulations: BAHAMAS + MACSIS, IllustrisTNG, and Magneticum ...Pathfinder. Applying localized linear regression, we extract halo mass-conditioned normalizations, slopes, and intrinsic covariance for (i) Nsat, the number of stellar mass-thresholded satellite galaxies within radius R200c of the halo; (ii) $M_{\star , \rm tot}$, the total stellar mass within that radius, and (iii) $M_{\star ,\rm BCG}$, the gravitationally bound stellar mass of the central galaxy within a $100 \, \rm kpc$ radius. The parameters show differences across the simulations, in part from numerical resolution, but there is qualitative agreement for the $N_{\rm sat}\!-\! M_{\star ,\rm BCG}$ correlation. Marginalizing over Mhalo, we find the Nsat kernel, $p(\ln N_{\rm sat}\, |\, M_{\rm halo}, z)$ to be consistently skewed left in all three simulations, with skewness parameter γ = −0.91 ± 0.02, while the $M_{\star , \rm tot}$ kernel shape is closer to lognormal. The highest resolution simulations find γ ≃ −0.8 for the z = 0 shape of the $M_{\star ,\rm BCG}$ kernel. We provide a Gaussian mixture fit to the low-redshift Nsat kernel as well as local linear regression parameters tabulated for $M_{\rm halo}\gt 10^{13.5} \, {\rm M}_\odot$ in all simulations.
We present a simultaneous analysis of galaxy cluster scaling relations between weak-lensing mass and multiple cluster observables, across a wide range of wavelengths, that probe both gas and stellar ...content. Our new hierarchical Bayesian model simultaneously considers the selection variable alongside all other observables in order to explicitly model intrinsic property covariance and account for selection effects. We apply this method to a sample of 41 clusters at 0.15 < |$z$| < 0.30, with a well-defined selection criteria based on RASS X-ray luminosity, and observations from Chandra/XMM, SZA, Planck, UKIRT, SDSS, and Subaru. These clusters have well-constrained weak-lensing mass measurements based on Subaru/Suprime-Cam observations, which serve as the reference masses in our model. We present 30 scaling relation parameters for 10 properties. All relations probing the intracluster gas are slightly shallower than self-similar predictions, in moderate tension with prior measurements, and the stellar fraction decreases with mass. K-band luminosity has the lowest intrinsic scatter with a 95th percentile of 0.16, while the lowest scatter gas probe is gas mass with a fractional intrinsic scatter of 0.16 ± 0.03. We find no distinction between the core-excised X-ray or high-resolution Sunyaev–Zel’dovich relations of clusters of different central entropy, but find with modest significance that higher entropy clusters have higher stellar fractions than their lower entropy counterparts. We also report posterior mass estimates from our likelihood model.
The largest clusters of galaxies in the Universe contain vast amounts of dark matter, plus baryonic matter in two principal phases, a majority hot gas component and a minority cold stellar phase ...comprising stars, compact objects, and low-temperature gas. Hydrodynamic simulations indicate that the highest-mass systems retain the cosmic fraction of baryons, a natural consequence of which is anti-correlation between the masses of hot gas and stars within dark matter halos of fixed total mass. We report observational detection of this anti-correlation based on 4 elements of a 9 × 9-element covariance matrix for nine cluster properties, measured from multi-wavelength observations of 41 clusters from the Local Cluster Substructure Survey. These clusters were selected using explicit and quantitative selection rules that were then encoded in our hierarchical Bayesian model. Our detection of anti-correlation is consistent with predictions from contemporary hydrodynamic cosmological simulations that were not tuned to reproduce this signal.
Abstract
We present the rhapsody-g suite of cosmological hydrodynamic zoom simulations of 10 massive galaxy clusters at the M
vir ∼ 1015 M⊙ scale. These simulations include cooling and subresolution ...models for star formation and stellar and supermassive black hole feedback. The sample is selected to capture the whole gamut of assembly histories that produce clusters of similar final mass. We present an overview of the successes and shortcomings of such simulations in reproducing both the stellar properties of galaxies as well as properties of the hot plasma in clusters. In our simulations, a long-lived cool-core/non-cool-core dichotomy arises naturally, and the emergence of non-cool cores is related to low angular momentum major mergers. Nevertheless, the cool-core clusters exhibit a low central entropy compared to observations, which cannot be alleviated by thermal active galactic nuclei feedback. For cluster scaling relations, we find that the simulations match well the M
500–Y
500 scaling of Planck Sunyaev–Zeldovich clusters but deviate somewhat from the observed X-ray luminosity and temperature scaling relations in the sense of being slightly too bright and too cool at fixed mass, respectively. Stars are produced at an efficiency consistent with abundance-matching constraints and central galaxies have star formation rates consistent with recent observations. While our simulations thus match various key properties remarkably well, we conclude that the shortcomings strongly suggest an important role for non-thermal processes (through feedback or otherwise) or thermal conduction in shaping the intracluster medium.
The massive dark matter haloes that host groups and clusters of galaxies have observable properties that appear to be lognormally distributed about power-law mean scaling relations in halo mass. ...Coupling this assumption with either quadratic or cubic approximations to the mass function in log space, we derive closed-form expressions for the space density of haloes as a function of multiple observables as well as forms for the low-order moments of properties of observable-selected samples. Using a Tinker mass function in a Λ cold dark matter cosmology, we show that the cubic analytic model reproduces results obtained from direct, numerical convolution at the 10 per cent level or better over nearly the full range of observables covered by current observations and for redshifts extending to z = 1.5. The model provides an efficient framework for estimating effects arising from selection and covariance among observable properties in survey samples.
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