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 $x,y,łambda$ 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.
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