Testing pre-main sequence models: the power of a Bayesian approach
  Pre-main sequence (PMS) models provide invaluable tools for the study of star
forming regions as they allow to assign masses and ages to young stars. Thus it
is of primary importance to test the models against observations of PMS stars
with dynamically determined mass. We developed a Bayesian method for testing
the present generation of PMS models which allows for a quantitative comparison
with observations, largely superseding the widely used isochrones and tracks
qualitative superposition.
  Using the available PMS data we tested the newest PISA PMS models
establishing their good agreement with the observations. The data cover a mass
range from ~0.3 to ~3.1 Msun, temperatures from ~3x10^3 to ~1.2x10^4 K and
luminosities from ~3x10^-2 to ~60 Lsun. Masses are correctly predicted within
20% of the observed values in most of the cases and for some of them the
difference is as small as 5%. Nevertheless some discrepancies are also observed
and critically discussed.
  By means of simulations, using typical observational errors, we evaluated the
spread of log \tau_sim - log \tau_rec, i.e. simulated minus recovered ages
distribution of the single objects. We also found that stars in binary systems
simulated as coeval might be recovered as non coeval, due to observational
errors. The actual fraction of fake non coevality is a complex function of the
simulated ages, masses and mass ratios. We demonstrated that it is possible to
recover the systems' ages with better precision than for single stars using the
composite age-probability distribution, i.e. the product of the components' age
distributions. Using this valuable tool we estimated the ages of the presently
observed PMS binary systems.
