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 us 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 masses. 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 good agreement with the observations. The data cover a mass range from similar to 0.3 to similar to 3.1 M-circle dot, temperatures from similar to 3 x 10(3) to similar to 1.2 x 10(4) K and luminosities from similar to 3 x 10(-2) to similar to 60 L-circle dot. Masses are correctly predicted within 20 per cent of the observed values in most of the cases, and for some of them the difference is as small as 5 per cent. 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) - og tau(rec), i.e. simulated - ecovered age 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.