GalPak^{3D}: A Bayesian Parametric Tool for Extracting Morphokinematics of Galaxies from 3D Data
Abstract
We present a method to constrain galaxy parameters directly from threedimensional data cubes. The algorithm compares directly the data with a parametric model mapped in x,y,λ coordinates. It uses the spectral linespread function and the spatial pointspread function (PSF) to generate a threedimensional 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 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 signaltonoise ratio (S/N) is greater than ˜3 pixel^{1} and that the ratio of galaxy halflight 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 data or on highquality, highS/N data to look for nonaxisymmetric structures in the residuals.
 Publication:

The Astronomical Journal
 Pub Date:
 September 2015
 DOI:
 10.1088/00046256/150/3/92
 arXiv:
 arXiv:1501.06586
 Bibcode:
 2015AJ....150...92B
 Keywords:

 methods: data analysis;
 methods: numerical;
 techniques: imaging spectroscopy;
 Astrophysics  Instrumentation and Methods for Astrophysics;
 Astrophysics  Astrophysics of Galaxies
 EPrint:
 16 pages, 10 figures, accepted to publication in AJ, revised version after proofs corrections. Algorithm available at http://galpak.irap.omp.eu