Dynamical Modeling of NGC 6809: Selecting the best model using Bayesian Inference

The precise cosmological origin of globular clusters remains uncertain, a situation hampered by the struggle of observational approaches in conclusively identifying the presence, or not, of dark matter in these systems. In this paper, we address this question through an analysis of the particular case of NGC 6809. While previous studies have performed dynamical modeling of this globular cluster using a small number of available kinematic data, they did not perform appropriate statistical inference tests for the choice of best model description; such statistical inference for model selection is important since, in general, different models can result in significantly different inferred quantities. With the latest kinematic data, we use Bayesian inference tests for model selection and thus obtain the best fitting models, as well as mass and dynamic mass-to-light ratio estimates. For this, we introduce a new likelihood function that provides more constrained distributions for the defining parameters of dynamical models. Initially we consider models with a known distribution function, and then model the cluster using solutions of the spherically symmetric Jeans equation; this latter approach depends upon the mass density profile and anisotropy $\beta$ parameter. In order to find the best description for the cluster we compare these models by calculating their Bayesian evidence. We find smaller mass and dynamic mass-to-light ratio values than previous studies, with the best fitting Michie model for a constant mass-to-light ratio of $\Upsilon = 0.90^{+0.14}_{-0.14}$ and $M_{\text{dyn}}=6.10^{+0.51}_{-0.88} \times 10^4 M_{\odot}$. We exclude the significant presence of dark matter throughout the cluster, showing that no physically motivated distribution of dark matter can be present away from the cluster core.Comment: 12 pages, 10 figures, accepted for publication in MNRA

Radio Galaxy Zoo: CLARAN - A deep learning classifier for radio morphologies

The upcoming next-generation large area radio continuum surveys can expect tens of millions of radio sources, rendering the traditional method for radio morphology classification through visual inspection unfeasible.We present CLARAN-Classifying Radio sources Automatically with Neural networks - a proof-of-concept radio source morphology classifier based upon the Faster Region-based Convolutional Neutral Networks method. Specifically, we train and test CLARAN on the FIRST and WISE (Wide-field Infrared Survey Explorer) images from the Radio Galaxy Zoo Data Release 1 catalogue. CLARAN provides end users with automated identification of radio source morphology classifications from a simple input of a radio image and a counterpart infrared image of the same region. CLARAN is the first open-source, endto- end radio source morphology classifier that is capable of locating and associating discrete and extended components of radio sources in a fast (<200 ms per image) and accurate (=90 per cent) fashion. Future work will improve CLARAN's relatively lower success rates in dealing with multisource fields and will enable CLARAN to identify sources on much larger fields without loss in classification accuracy

Selecting Sagittarius : identification and chemical characterization of the Sagittarius stream

Wrapping around the Milky Way, the Sagittarius stream is the dominant substructure in the halo. Our statistical selection method has allowed us to identify 106 highly likely members of the Sagittarius stream. Spectroscopic analysis of metallicity and kinematics of all members provides us with a new mapping of the Sagittarius stream. We find correspondence between the velocity distribution of stream stars and those computed for a triaxial model of the Milky Way dark matter halo. The Sagittarius trailing arm exhibits a metallicity gradient, ranging from −0.59 to −0.97 dex over 142°. This is consistent with the scenario of tidal disruption from a progenitor dwarf galaxy that possessed an internal metallicity gradient. We note high metallicity dispersion in the leading arm, causing a lack of detectable gradient and possibly indicating orbital phase mixing. We additionally report on a potential detection of the Sextans dwarf spheroidal in our data

A novel JEANS analysis of the Fornax dwarf using evolutionary algorithms: mass follows light with signs of an off-centre merger

Dwarf galaxies, among the most dark matter dominated structures of our Universe, are excellent test-beds for dark matter theories. Unfortunately, mass modelling of these systems suffers from the well-documented mass-velocity anisotropy degeneracy. For the case of spherically symmetric systems, we describe a method for non-parametric modelling of the radial and tangential velocity moments. The method is a numerical velocity anisotropy "inversion", with parametric mass models, where the radial velocity dispersion profile, σ 2 π , is modelled as a B-spline, and the optimization is a three-step process that consists of (i) an evolutionary modelling to determine the mass model form and the best B-spline basis to represent σ 2 π ; (ii) an optimization of the smoothing parameters and (iii) a Markov chain Monte Carlo analysis to determine the physical parameters. The mass-anisotropy degeneracy is reduced into mass model inference, irrespective of kinematics. We test our method using synthetic data. Our algorithm constructs the best kinematic profile and discriminates between competing dark matter models. We apply our method to the Fornax dwarf spheroidal galaxy. Using a King brightness profile and testing various dark matter mass models, our model inference favours a simple mass-follows-light system. We find that the anisotropy profile of Fornax is tangential (β(r) < 0) and we estimate a total mass of M tot = 1.613 +0.050 -0.075 × 10 8 M ⊙ , and a mass-to-light ratio of υ V = 8.93 +0.32 -0.47 (M ⊙ /L ⊙ ). The algorithm we present is a robust and computationally inexpensive method for non-parametric modelling of spherical clusters independent of the mass-anisotropy degeneracy