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We introduce BAYES-LOSVD, a novel implementation of the non-parametricextraction of line-of-sight velocity distributions (LOSVDs) in galaxies. Weemploy bayesian inference to obtain robust LOSVDs and associated uncertainties.Our method relies on principal component analysis to reduce the dimensionalityof the base of templates required for the extraction and thus increase theperformance of the code. In addition, we implement several options toregularise the output solutions. Our tests, conducted on mock spectra, confirmthe ability of our approach to model a wide range of LOSVD shapes, overcominglimitations of the most widely used parametric methods (e.g. Gauss-Hermiteexpansion). We present examples of LOSVD extractions for real galaxies withknown peculiar LOSVD shapes, i.e. NGC4371, IC0719 and NGC4550, using MUSE andSAURON integral-field unit (IFU) data. Our implementation can also handle datafrom other popular IFU surveys (e.g. ATLAS3D, CALIFA, MaNGA, SAMI). Details ofthe code and relevant documentation are freely available to the community inthe dedicated repositories.

BAYES-LOSVD: a bayesian framework for non-parametric extraction of the line-of-sight velocity distribution of galaxies