Transforming measured luminosities into masses is a complex exercise. One must at the same time find the right combination of effective temperature, age and extinction towards the source that best fit the observed luminosity. Astronomers usually have luminosity measurements in many filters, each with its own uncertainty, adding to the complexity.
Sakam is a Bayesian inference code that samples the cluster member's joint posterior of mass and extinction given multi-wavelength photometry, Kalkayotl distances, and a theoretical isochrone models (from the literature). It is able to deal with outlier measurements and underestimated uncertainties thanks to its robust data modelling.