Spectral classification of young stars using conditional invertible neural networks II. Application to Trumpler 14 in Carina

Abstract

We introduce an updated version of our deep learning tool that predicts stellar parameters from the optical spectra of young low-mass stars with intermediate spectral resolution. We adopt a conditional invertible neural network (cINN) architecture to infer the posterior distribution of stellar parameters and train our cINN on two Phoenix stellar atmosphere model libraries (Settl and Dusty). Compared to the cINNs presented in our first study, the updated cINN considers the influence of the relative flux error on the parameter estimation and predicts an additional fourth parameter, veiling. We test the performance of cINN on synthetic test models to quantify the intrinsic error of the cINN as a function of relative flux error and on 36 class III template stars to validate the performance on real spectra. Using our cINN, we estimate the stellar parameters of young stars in Trumpler 14 (Tr14) in the Carina Nebula Complex, observed with VLT-MUSE, and compare them with those derived using the classical template fitting method. We provide Teff, log g, Av, and veiling values measured by our cINN as well as stellar ages and masses derived from the HR diagram. Our parameter estimates generally agree well with those measured by template fitting. However, for K- and G-type stars, the Teff derived from template fitting is, on average, 2-3 subclasses hotter than the cINN estimates, while the corresponding veiling values from template fitting appear to be underestimated compared to the cINN predictions. We obtain an average age of 0.7(+3.2)(-0.6) Myr for the Tr14 stars. By examining the impact of veiling on the equivalent width-based classification, we demonstrate that the main cause of temperature overestimation for K- and G-type stars in the previous study is that veiling and effective temperature are not considered simultaneously in their process.