BAYES-LOSVD: a bayesian framework for non-parametric extraction of the
  line-of-sight velocity distribution of galaxies
  We introduce BAYES-LOSVD, a novel implementation of the non-parametric
extraction of line-of-sight velocity distributions (LOSVDs) in galaxies. We
employ bayesian inference to obtain robust LOSVDs and associated uncertainties.
Our method relies on principal component analysis to reduce the dimensionality
of the base of templates required for the extraction and thus increase the
performance of the code. In addition, we implement several options to
regularise the output solutions. Our tests, conducted on mock spectra, confirm
the ability of our approach to model a wide range of LOSVD shapes, overcoming
limitations of the most widely used parametric methods (e.g. Gauss-Hermite
expansion). We present examples of LOSVD extractions for real galaxies with
known peculiar LOSVD shapes, i.e. NGC4371, IC0719 and NGC4550, using MUSE and
SAURON integral-field unit (IFU) data. Our implementation can also handle data
from other popular IFU surveys (e.g. ATLAS3D, CALIFA, MaNGA, SAMI). Details of
the code and relevant documentation are freely available to the community in
the dedicated repositories.
