Testing pre-main sequence models: the power of a Bayesian approach
Mario Gennaro (1), Pier Giorgio Prada Moroni (2,3), Emanuele Tognelli, (2,3) ((1) Max-Planck-Institut fuer Astronomie, Heidelberg, (2) Universita', di Pisa, (3) Istituto Nazionale di Fisica Nucleare - Sezione di Pisa)

TL;DR
This paper introduces a Bayesian method for testing pre-main sequence models against observations, demonstrating improved accuracy in mass and age estimations of young stars, and analyzing the effects of observational errors and binarity.
Contribution
The authors developed a Bayesian approach for quantitatively testing PMS models, surpassing traditional isochrone methods, and applied it to evaluate the PISA models with real and simulated data.
Findings
Masses are predicted within 20% of observed values.
Stars in binary systems can appear non coeval due to observational errors.
The Bayesian method improves age and mass estimates for PMS stars.
Abstract
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%.…
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