NGC 2264 T Tauri star properties

Large statistical samples of young stars across star-forming regions are needed to trigger a breakthrough in our understanding, but most optical studies are based on a wide variety of spectrographs and analysis methods, which introduces large biases. Here we show how graphical Bayesian networks can be employed to construct a hierarchical probabilistic model which allows pre-main-sequence ages, masses, accretion rates and extinctions to be estimated using two widely available photometric survey data bases (Isaac Newton Telescope Photometric H{alpha} Survey r'/H{alpha}/i' and Two Micron All Sky Survey J-band magnitudes).

Because our approach does not rely on spectroscopy, it can easily be applied to homogeneously study the large number of clusters for which Gaia will yield membership lists. We explain how the analysis is carried out using the Markov chain Monte Carlo method and provide python source code. We then demonstrate its use on 587 known low-mass members of the star-forming region NGC 2264 (Cone Nebula), arriving at a median age of 3.0Myr, an accretion fraction of 20+/-2 per cent and a median accretion rate of 10-8.4M{sun}/yr.