Abstract
Halos of similar mass and redshift exhibit a large degree of variability in their differential properties, such as dark matter, hot gas, and stellar mass density profiles. This variability ...is an indicator of diversity in the formation history of these dark matter halos that is reflected in the coupling of scatters about the mean relations. In this work, we show that the strength of this coupling depends on the scale at which halo profiles are measured. By analyzing the outputs of the IllustrisTNG hydrodynamical cosmological simulations, we report the radial- and mass-dependent couplings between the dark matter, hot gas, and stellar mass radial density profiles utilizing the population diversity in dark matter halos. We find that for the same mass halos, the scatters in the density of baryons and dark matter are strongly coupled at large scales (
r
>
R
200
), but the coupling between gas and dark matter density profiles fades near the core of halos (
r
< 0.3
R
200
). We then show that the correlation between halo profile and integrated quantities induces a radius-dependent additive bias in the profile observables of halos when halos are selected on properties other than their mass. We discuss the impact of this effect on cluster abundance and cross-correlation cosmology with multiwavelength cosmological surveys.
We demonstrate the ability of convolutional neural networks (CNNs) to mitigate systematics in the virial scaling relation and produce dynamical mass estimates of galaxy clusters with remarkably low ...bias and scatter. We present two models, CNN1D and CNN2D, which leverage this deep learning tool to infer cluster masses from distributions of member galaxy dynamics. Our first model, CNN1D, infers cluster mass directly from the distribution of member galaxy line-of-sight velocities. Our second model, CNN2D, extends the input space of CNN1D to learn on the joint distribution of galaxy line-of-sight velocities and projected radial distances. We train each model as a regression over cluster mass using a labeled catalog of realistic mock cluster observations generated from the MultiDark simulation and UniverseMachine catalog. We then evaluate the performance of each model on an independent set of mock observations selected from the same simulated catalog. The CNN models produce cluster mass predictions with lognormal residuals of scatter as low as 0.132 dex, greater than a factor of 2 improvement over the classical M- power-law estimator. Furthermore, the CNN model reduces prediction scatter relative to similar machine-learning approaches by up to 17% while executing in drastically shorter training and evaluation times (by a factor of 30) and producing considerably more robust mass predictions (improving prediction stability under variations in galaxy sampling rate by 30%).
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 present a novel population-based Bayesian inference approach to model the average and population variance of the spatial distribution of a set of observables from ensemble analysis of low ...signal-to-noise-ratio measurements. The method consists of (1) inferring the average profile using Gaussian processes and (2) computing the covariance of the profile observables given a set of independent variables. Our model is computationally efficient and capable of inferring average profiles of a large population size from noisy measurements, without stacking data or parameterizing the shape of the mean profile. We demonstrate the performance of our method using dark matter, gas, and stellar profiles extracted from hydrodynamical cosmological simulations of galaxy formation. P
opulation
P
rofile
E
stimator
is publicly available in a GitHub repository. Our new method should be useful for measuring the spatial distribution and internal structure of a variety of astrophysical systems using large astronomical surveys.
Abstract
We study methods for reconstructing Bayesian uncertainties on dynamical mass estimates of galaxy clusters using convolutional neural networks (CNNs). We discuss the statistical background of ...approximate Bayesian neural networks and demonstrate how variational inference techniques can be used to perform computationally tractable posterior estimation for a variety of deep neural architectures. We explore how various model designs and statistical assumptions impact prediction accuracy and uncertainty reconstruction in the context of cluster mass estimation. We measure the quality of our model posterior recovery using a mock cluster observation catalog derived from the MultiDark simulation and UniverseMachine catalog. We show that approximate Bayesian CNNs produce highly accurate dynamical cluster mass posteriors. These model posteriors are log-normal in cluster mass and recover 68% and 90% confidence intervals to within 1% of their measured value. We note how this rigorous modeling of dynamical mass posteriors is necessary for using cluster abundance measurements to constrain cosmological parameters.
ABSTRACT
Cold dark matter model predicts that the large-scale structure grows hierarchically. Small dark matter haloes form first. Then, they grow gradually via continuous merger and accretion. These ...haloes host the majority of baryonic matter in the Universe in the form of hot gas and cold stellar phase. Determining how baryons are partitioned into these phases requires detailed modelling of galaxy formation and their assembly history. It is speculated that formation time of the same mass haloes might be correlated with their baryonic content. To evaluate this hypothesis, we employ haloes of mass above $10^{14}\, \mathrm{M}_{\odot }$ realized by TNG300 solution of the IllustrisTNG project. Formation time is not directly observable. Hence, we rely on the magnitude gap between the brightest and the fourth brightest halo galaxy member, which is shown that traces formation time of the host halo. We compute the conditional statistics of the stellar and gas content of haloes conditioned on their total mass and magnitude gap. We find a strong correlation between magnitude gap and gas mass, BCG stellar mass, and satellite galaxies stellar mass, but not the total stellar mass of halo. Conditioning on the magnitude gap can reduce the scatter about halo property–halo mass relation and has a significant impact on the conditional covariance. Reduction in the scatter can be as significant as 30 per cent, which implies more accurate halo mass prediction. Incorporating the magnitude gap has a potential to improve cosmological constraints using halo abundance and allows us to gain insight into the baryon evolution within these systems.
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 eROSITA X-ray telescope, launched in 2019, is predicted to observe roughly 100,000 galaxy clusters. Follow-up observations of these clusters from Chandra, for example, will be needed to ...resolve outstanding questions about galaxy cluster physics. Deep Chandra cluster observations are expensive, and it is unfeasible to follow up every eROSITA cluster, therefore the objects that are chosen for follow-up must be chosen with care. To address this, we have developed an algorithm for predicting longer-duration, background-free observations, based on mock eROSITA observations. We make use of the hydrodynamic cosmological simulation
Magneticum
, simulate eROSITA instrument conditions using
SIXTE
, and apply a novel convolutional neural network to output a deep Chandra-like “super observation” of each cluster in our simulation sample. Any follow-up merit assessment tool should be designed with a specific use case in mind; our model produces observations that accurately and precisely reproduce the cluster morphology, which is a critical ingredient for determining a cluster’s dynamical state and core type. Our model will advance our understanding of galaxy clusters by improving follow-up selection, and it demonstrates that image-to-image deep learning algorithms are a viable method for simulating realistic follow-up observations.
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.
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