2024MNRAS.527.2585L

We test the viability of training machine learning algorithms with synthetic $\rm H\, \alpha$ line profiles to determine the inclination angles (the angle between the central B star's rotation axis and the observer's line of sight) of B emission (Be) stars from a single observed medium-resolution, moderate signal-to-noise ratio spectrum. The performances of three different machine learning algorithms were compared: neural networks tasked with regression, neural networks tasked with classification, and support vector regression. Of these three algorithms, neural networks tasked with regression consistently outperformed the other methods with a root mean squared error of 7.6° on an observational sample of 92 galactic Be stars with inclination angles known from direct $\rm H\, \alpha$ profile fitting, from the spectroscopic signature of gravitational darkening, and, in a few cases, from interferometric observations that resolved the disc. The trained neural networks enable a quick and useful determination of the inclination angles of observed Be stars, which can be used to search for correlated spin axes in young open clusters or to extract an equatorial rotation velocity from a measurement of vsin i.