# A quick overview¶

The algorithm directly compares data-cubes with a disk parametric model which has 9 or 10 free parameters [which can also be fixed independently]. The algorithm uses a Markov Chain Monte Carlo (MCMC) approach with non-traditional sampling laws in order to efficiently probe the parameter space. More importantly, it uses the knowledge of the 3-dimensional spread-function to return the intrinsic galaxy properties and the intrinsic data-cube. The 3D spread-function class is flexible enough to handle any instrument.

One can use such an algorithm to constrain simultaneously the kinematics and morphological parameters of (non-merging, i.e. regular) 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.

The algorithm can be (roughly) summarized as :

- Pick new random galaxy parameters using uniform priors [boundaries can be set]
- Create clean cube from parameters and convolve it with instrument’s PSF+LSF
- Measure closeness of resulting convolved cube to input cube
- Accept/Reject galaxy parameters using Metropolis-Hasting algorithm
- Goto 1. until max iterations are reached
- Fill output attributes with data (chain, cubes, etc.)
- Compute and return best fit galaxy parameters from chain

Warning

The output flux is in the pixel units, which may need to multiplied by CDELT3.

Warning

- The algorithm should never be used blindly and we stress that one should always
- look at the convergence of the parameters using
`plot_mcmc()`

, - investigate possible covariance in the parameters using
`plot_correlations()`

and/or fix a parameter with the option known_parameters to remove the degenerancy, - adjust the MCMC algorithm with the option random_scale (lower than 1 [default] for higher acceptance rate and vice versa) to ensure an acceptance rate of 30 to 50%.

- look at the convergence of the parameters using

## Input Cubes supported¶

- Any fits cube can be used provided that the z-axis represents wavelengths or frequencies.
- Any units are normally accepted as the algorithm works in velocity space (dlamba/lamba or dfrequency/frequency). Check the Instrument.z_step_kms value which is critial for the kinematic parameters.
- If the header is incomplete (CRPIX3, CDELT3, CRVAL3, CUNIT3), the algorithm will try to use the default values assigned to the instrument. The user can specify these directly.
- If the header is complete, the instrument default pixel sizes will be over-written by the the information from the cube header.

Warning

Pay attention to the LSF FWHM, which should be specified in the same units as the cube.

## Instruments supported¶

- ALMA
set lsf_fwhm to 1 cdelt (default) or less

SINFONI (J250, H250 and K250 modes)

- MUSE (WFM and NFW modes)
default lsf_fwhm is 2.67 Angstrom

- KMOS
default xy_step is 0.2 arcsecs

MANGA (soon)

Note

The default Instrument values will be overridden by the cube header info (CRPIX, CDELT etc.) if present.