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Register a new userVelocity dispersion and dynamical mass for 270 galaxy clusters in the Planck PSZ1 catalogue
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We present the velocity dispersion and dynamical mass estimates for 270 galaxy clusters included in the first Planck Sunyaev-Zeldovich (SZ) source catalogue, the PSZ1. Part of the results presented here were achieved during a two-year observational program, the ITP, developed at the Roque de los Muchachos Observatory (La Palma, Spain). In the ITP we carried out a systematic optical follow-up campaign of all the 212 unidentified PSZ1 sources in the northern sky that have a declination above $-15^circ$ and are without known counterparts at the time of the publication of the catalogue. We present for the first time the velocity dispersion and dynamical mass of 58 of these ITP PSZ1 clusters, plus 35 newly discovered clusters that are not associated with the PSZ1 catalogue. Using Sloan Digital Sky Survey (SDSS) archival data, we extend this sample, including 212 already confirmed PSZ1 clusters in the northern sky. Using a subset of 207 of these galaxy clusters, we constrained the $M_{rm SZ}$--$M_{rm dyn}$ scaling relation, finding a mass bias of $(1-B) = 0.83pm0.07$(stat)$pm0.02$(sys). We show that this value is consistent with other results in the literature that were obtained with different methods (X-ray, dynamical masses, or weak-lensing mass proxies). This result cannot dissolve the tension between primordial cosmic microwave background anisotropies and cluster number counts in the $Omega_{rm M}$--$sigma_8$ plane.
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In light of the tension in cosmological constraints reported by the Planck team between their SZ-selected cluster counts and Cosmic Microwave Background (CMB) temperature anisotropies, we compare the Planck cluster mass estimates with robust, weak-lensing mass measurements from the Weighing the Giants (WtG) project. For the 22 clusters in common between the Planck cosmology sample and WtG, we find an overall mass ratio of $left< M_{Planck}/M_{rm WtG} right> = 0.688 pm 0.072$. Extending the sample to clusters not used in the Planck cosmology analysis yields a consistent value of $left< M_{Planck}/M_{rm WtG} right> = 0.698 pm 0.062$ from 38 clusters in common. Identifying the weak-lensing masses as proxies for the true cluster mass (on average), these ratios are $sim 1.6sigma$ lower than the default mass bias of 0.8 assumed in the Planck cluster analysis. Adopting the WtG weak-lensing-based mass calibration would substantially reduce the tension found between the Planck cluster count cosmology results and those from CMB temperature anisotropies, thereby dispensing of the need for new physics such as uncomfortably large neutrino masses (in the context of the measured Planck temperature anisotropies and other data). We also find modest evidence (at 95 per cent confidence) for a mass dependence of the calibration ratio and discuss its potential origin in light of systematic uncertainties in the temperature calibration of the X-ray measurements used to calibrate the Planck cluster masses. Our results exemplify the critical role that robust absolute mass calibration plays in cluster cosmology, and the invaluable role of accurate weak-lensing mass measurements in this regard.
We present a study of the statistical properties of three velocity dispersion and mass estimators, namely biweight, gapper and standard deviation, in the small number of galaxies regime ($N_{rm gal} le 75$). Using a set of 73 numerically simulated galaxy clusters, we characterise the statistical bias and the variance for the three estimators, both in the determination of the velocity dispersion and the dynamical mass of the clusters via the $sigma-M$ relation. The results are used to define a new set of unbiased estimators, that are able to correct for those statistical biases with a minimal increase of the associated variance. The numerical simulations are also used to characterise the impact of velocity segregation in the selection of cluster members, and the impact of using cluster members within different physical radii from the cluster centre. The standard deviation is found to be the lowest variance estimator. The selection of galaxies within the sub-sample of the most massive galaxies in the cluster introduces a $2,$% bias in the velocity dispersion estimate when calculated using a quarter of the most massive cluster members. We also find a dependence of the velocity dispersion estimate on the aperture radius as a fraction of $R_{200}$, consistent with previous results. The proposed set of unbiased estimators effectively provides a correction of the velocity dispersion and mass estimates from all those effects in the small number of cluster members regime. This is tested by applying the new estimators to a subset of simulated observations. Although for a single galaxy cluster the statistical and physical effects discussed here are comparable or slightly smaller than the bias introduced by interlopers, they will be of relevance when dealing with ensemble properties and scaling relations for large cluster samples (Abridged).
We present a method to derive the dynamical mass of face-on galaxy disks using their neutral hydrogen (HI) velocity dispersion. We have applied the method to nearby, gas rich galaxies that have extended HI gas disks and have low inclinations. The galaxy sample includes 4 large disk galaxies; NGC628, NGC6496, NGC3184, NGC4214 and 3 dwarf galaxies DDO46, DDO63 and DDO187. We have used archival data from the THINGS and LITTLE THINGS surveys to derive the HI gas distributions and SPITZER mid-infrared images to determine the stellar disk mass distributions. We examine the disk dynamical and baryonic mass ratios in the extreme outer disks where there is HI gas but no visible stellar disk. We find that for the large galaxies the disk dynamical and Hi gas mass surface densities are comparable in the outer disks. But in the smaller dwarf galaxies, for which the total HI gas mass dominates the baryonic mass i.e. M(HI)>M(stars), the disk dynamical mass is much larger than the baryonic mass. For these galaxies there must either be a very low luminosity stellar disk which provides the vertical support for the HI gas disk or there is halo dark matter associated with their disks, which is possible if the halo has an oblate shape so that the inner part of the dark matter halo is concentrated around the disk. Our results are important for explaining the equilibrium of HI disks in the absence of stellar disks, and is especially important for gas rich, dwarf galaxies that appear to have significant dark matter masses associated with their disks.
We present a modern machine learning approach for cluster dynamical mass measurements that is a factor of two improvement over using a conventional scaling relation. Different methods are tested against a mock cluster catalog constructed using halos with mass >= 10^14 Msolar/h from Multidarks publicly-available N-body MDPL halo catalog. In the conventional method, we use a standard M(sigma_v) power law scaling relation to infer cluster mass, M, from line-of-sight (LOS) galaxy velocity dispersion, sigma_v. The resulting fractional mass error distribution is broad, with width=0.87 (68% scatter), and has extended high-error tails. The standard scaling relation can be simply enhanced by including higher-order moments of the LOS velocity distribution. Applying the kurtosis as a correction term to log(sigma_v) reduces the width of the error distribution to 0.74 (16% improvement). Machine learning can be used to take full advantage of all the information in the velocity distribution. We employ the Support Distribution Machines (SDMs) algorithm that learns from distributions of data to predict single values. SDMs trained and tested on the distribution of LOS velocities yield width=0.46 (47% improvement). Furthermore, the problematic tails of the mass error distribution are effectively eliminated. Decreasing cluster mass errors will improve measurements of the growth of structure and lead to tighter constraints on cosmological parameters.
We present an algorithm for inferring the dynamical mass of galaxy clusters directly from their respective phase-space distributions, i.e. the observed line-of-sight velocities and projected distances of galaxies from the cluster centre. Our method employs normalizing flows, a deep neural network capable of learning arbitrary high-dimensional probability distributions, and inherently accounts, to an adequate extent, for the presence of interloper galaxies which are not bounded to a given cluster, the primary contaminant of dynamical mass measurements. We validate and showcase the performance of our neural flow approach to robustly infer the dynamical mass of clusters from a realistic mock cluster catalogue. A key aspect of our novel algorithm is that it yields the probability density function of the mass of a particular cluster, thereby providing a principled way of quantifying uncertainties, in contrast to conventional machine learning approaches. The neural network mass predictions, when applied to a contaminated catalogue with interlopers, have a mean overall logarithmic residual scatter of 0.028 dex, with a log-normal scatter of 0.126 dex, which goes down to 0.089 dex for clusters in the intermediate to high mass range. This is an improvement by nearly a factor of four relative to the classical cluster mass scaling relation with the velocity dispersion, and outperforms recently proposed machine learning approaches. We also apply our neural flow mass estimator to a compilation of galaxy observations of some well-studied clusters with robust dynamical mass estimates, further substantiating the efficacy of our algorithm.
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