Most of the super massive black hole mass estimates based on stellar kinematics use the assumption that galaxies are axisymmetric oblate spheroids or spherical. Here we use fully general triaxial orbit-based models to explore the effect of relaxing the axisymmetric assumption on the previously studied galaxies M32 and NGC 3379. We find that M32 can only be modeled accurately using an axisymmetric shape viewed nearly edge-on and our black hole mass estimate is identical to previous studies. When the observed 5 degrees kinematical twist is included in our model of NGC 3379, the best shape is mildly triaxial and we find that our best-fitting black hole mass estimate doubles with respect to the axisymmetric model. This particular black hole mass estimate is still within the errors of that of the axisymmetric model and consistent with the M-sigma relationship. However, this effect may have a pronounced impact on black hole demography, since roughly a third of the most massive galaxies are strongly triaxial.
Weak-lensing measurements of the masses of galaxy clusters are commonly based on the assumption of spherically symmetric density profiles. Yet, the cold dark matter model predicts the shapes of dark matter halos to be triaxial. Halo triaxiality, and the orientation of the major axis with respect to the line of sight, are expected to be the leading cause of intrinsic scatter in weak-lensing mass measurements. The shape of central cluster galaxies (Brightest Cluster Galaxies; BCGs) is expected to follow the shape of the dark matter halo. Here we investigate the use of BCG ellipticity as predicto…
Estimating Dark Matter Halo Masses in Simulated Galaxy Clusters with Graph Neural Networks
Steward Observatory（斯图尔德天文台）；University of Arizona（亚利桑那大学）；Space Telescope Science Institute（空间望远镜科学研究所）；Universität Heidelberg（海德堡大学）；Max-Planck-Institut für Astronomie（马克斯·普朗克天文研究所）
The total masses of galaxy clusters characterize many aspects of astrophysics and the underlying cosmology. It is crucial to obtain reliable and accurate mass estimates for numerous galaxy clusters over a wide range of redshifts and mass scales. We present a transfer-learning approach to estimate cluster masses using the ugriz-band images in the SDSS Data Release 12. The target masses are derived from X-ray or SZ measurements that are only available for a small subset of the clusters. We designed a semi-supervised deep learning model consisting of two convolutional neural networks. In the firs…
The University of Western Australia（西澳大学）；International Centre for Radio Astronomy Research (ICRAR)（国际射电天文学研究中心（ICRAR））
We present a new method by which the total masses of galaxies including dark matter can be estimated from the kinematics of their globular cluster systems (GCSs). In the proposed method, we apply the convolutional neural networks (CNNs) to the two-dimensional (2D) maps of line-of-sight-velocities ($V$) and velocity dispersions ($σ$) of GCSs predicted from numerical simulations of disk and elliptical galaxies. In this method, we first train the CNN using either only a larger number ($\sim 200,000$) of the synthesized 2D maps of $σ$ ("one-channel") or those of both $σ$ and $V$ ("two-channel"). T…
Galaxy clusters are useful laboratories to investigate the evolution of the Universe, and accurately measuring their total masses allows us to constrain important cosmological parameters. However, estimating mass from observations that use different methods and spectral bands introduces various systematic errors. This paper evaluates the use of a Convolutional Neural Network (CNN) to reliably and accurately infer the masses of galaxy clusters from the Compton-y parameter maps provided by the Planck satellite. The CNN is trained with mock images generated from hydrodynamic simulations of galaxy…
We present the weak lensing mass calibration of the stellar mass based $μ_{\star}$ mass proxy for redMaPPer galaxy clusters in the Dark Energy Survey Year 1. For the first time we are able to perform a calibration of $μ_{\star}$ at high redshifts, $z>0.33$. In a blinded analysis, we use $\sim 6,000$ clusters split into 12 subsets spanning the ranges $0.1 \leqslant z<0.65$ and $μ_{\star}$ up to $\sim 5.5 \times 10^{13} M_{\odot}$, and infer the average masses of these subsets through modelling of their stacked weak lensing signal. In our model we account for the following sources of systematic …
