%0 Generic %1 bouche2015galpak3d %A Bouché, N. %A Carfantan, H. %A Schroetter, I. %A Michel-Dansac, L. %A Contini, T. %D 2015 %K bayesian galpak3d parametric tool %T GalPak3D: A Bayesian parametric tool for extracting morpho-kinematics of galaxies from 3D data %U http://arxiv.org/abs/1501.06586 %X We present a method to constrain galaxy parameters directly from 3-dimensional data-cubes. The algorithm compares directly the data-cube with a parametric model mapped in $x,y,łambda$ coordinates. It uses the spectral Line Spread Function (LSF) and the spatial Point Spread Function (PSF) to generate a 3-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 non-traditional 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) within 20%, irrespectively of the PSF in seeing (up to 1.2") provided that the maximum signal-to-noise (SNR) is greater than $\sim3$ pix$^-1$ and that the galaxy half-light radius ($R_1/2$) to seeing ratio (FWHM) is greater than about 0.75. One can use such algorithm to constrain simultaneously the kinematics and morphological parameters of (non-merging) galaxies observed in non optimal seeing conditions. The algorithm can also be used on Adaptive-Optics (AO) data or on high-quality, high-SNR data to look for non-axisymmetric structures in the residuals.

@misc{bouche2015galpak3d, abstract = {We present a method to constrain galaxy parameters directly from 3-dimensional data-cubes. The algorithm compares directly the data-cube with a parametric model mapped in $x,y,\lambda$ coordinates. It uses the spectral Line Spread Function (LSF) and the spatial Point Spread Function (PSF) to generate a 3-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 non-traditional 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) within 20%, irrespectively of the PSF in seeing (up to 1.2") provided that the maximum signal-to-noise (SNR) is greater than $\sim3$ pix$^{-1}$ and that the galaxy half-light radius ($R_{1/2}$) to seeing ratio (FWHM) is greater than about 0.75. One can use such algorithm to constrain simultaneously the kinematics and morphological parameters of (non-merging) galaxies observed in non optimal seeing conditions. The algorithm can also be used on Adaptive-Optics (AO) data or on high-quality, high-SNR data to look for non-axisymmetric structures in the residuals.}, added-at = {2015-01-28T10:28:17.000+0100}, author = {Bouché, N. and Carfantan, H. and Schroetter, I. and Michel-Dansac, L. and Contini, T.}, biburl = {https://www.bibsonomy.org/bibtex/25b60ab98e286f727beee8aa6a1bc8e6b/miki}, description = {[1501.06586] GalPak3D: A Bayesian parametric tool for extracting morpho-kinematics of galaxies from 3D data}, interhash = {c967f406c2c19dc48117eb7b60e3a4aa}, intrahash = {5b60ab98e286f727beee8aa6a1bc8e6b}, keywords = {bayesian galpak3d parametric tool}, note = {cite arxiv:1501.06586Comment: 16 pages, 10 figures, submitted to AJ, revised version after referee report. Algorithm available at http://gapak.irap.omp.eu}, timestamp = {2015-01-28T10:28:17.000+0100}, title = {GalPak3D: A Bayesian parametric tool for extracting morpho-kinematics of galaxies from 3D data}, url = {http://arxiv.org/abs/1501.06586}, year = 2015 }