Comparison of dust-to-gas ratios in luminous, ultraluminous, and hyperluminous infrared galaxies

The dust-to-gas ratios in three different samples of luminous, ultraluminous, and hyperluminous infrared galaxies are calculated by modelling their radio to soft X-ray spectral energy distributions using composite models which account for the photoionizing radiation from HII regions, starbursts, or AGNs, and for shocks. The models are limited to a set which broadly reproduces the mid-IR fine structure line ratios of local, IR bright, starburst galaxies. The results show that two types of clouds contribute to the IR emission. Those characterized by low shock velocities and low preshock densities explain the far-IR dust emission, while those with higher velocities and densities contribute to mid-IR dust emission. An AGN is found in nearly all of the ultraluminous IR galaxies and in half of the luminous IR galaxies of the sample. High IR luminosities depend on dust-to-gas ratios of about 0.1 by mass, however, most hyperluminous IR galaxies show dust-to-gas ratios much lower than those calculated for the luminous and ultraluminous IR galaxies.Comment: 19 pages+ 7 figures. in press in A

GalPak3D: A Bayesian parametric tool for extracting morpho-kinematics of galaxies from 3D data

We present a method to constrain galaxy parameters directly from three-dimensional data cubes. The algorithm compares directly the data with a parametric model mapped in $x,y,\lambda$ coordinates. It uses the spectral lines-spread function (LSF) and the spatial point-spread function (PSF) to generate a three-dimensional kernel whose characteristics are instrument specific or user generated. The algorithm returns the intrinsic modeled properties along with both an `intrinsic' model data cube and the modeled galaxy convolved with the 3D-kernel. The algorithm uses a Markov Chain Monte Carlo (MCMC) approach with a nontraditional proposal distribution in order to efficiently probe the parameter space. We demonstrate the robustness of the algorithm using 1728 mock galaxies and galaxies generated from hydrodynamical simulations in various seeing conditions from 0.6" to 1.2". We find that the algorithm can recover the morphological parameters (inclination, position angle) to within 10% and the kinematic parameters (maximum rotation velocity) to within 20%, irrespectively of the PSF in seeing (up to 1.2") provided that the maximum signal-to-noise ratio (SNR) is greater than $\sim3$ pixel$^{-1}$ and that the ratio of the galaxy half-light radius to seeing radius is greater than about 1.5. One can use such an algorithm to constrain simultaneously the kinematics and morphological parameters of (nonmerging) galaxies observed in nonoptimal seeing conditions. The algorithm can also be used on adaptive-optics (AO) data or on high-quality, high-SNR data to look for nonaxisymmetric structures in the residuals.Comment: 16 pages, 10 figures, accepted to publication in AJ, revised version after proofs corrections. Algorithm available at http://galpak.irap.omp.e