Determining stellar properties of massive stars in NGC346 in the SMC with a Bayesian statistic technique

Context. NGC 346 is a young cluster with numerous hot OB stars. It is part of the Small Magellanic Cloud (SMC), and has an average metallicity that is one-seventh of the Milky Way’s. A detailed study of its stellar content provides a unique opportunity to understand the stellar and wind properties of massive stars in low-metallicity environments, and enables us to improve our understanding of star formation and stellar evolution. Aims. The fundamental stellar parameters defining a star’s spectral appearance are its effective surface temperature, surface gravity, and projected rotational velocity. Unfortunately, these parameters cannot be obtained independently from only H and He spectral features as they are partially degenerate. With this work we aim to overcome this degeneracy by applying a newly developed Bayesian statistic technique that can fit these three parameters simultaneously. Methods. Multi-epoch optical spectra are used in combination with a Bayesian statistic technique to fit stellar properties based on a publicly available grid of synthetic spectra of stellar atmospheres. The use of all of the multi-epoch observations simultaneously allows the identification of binaries. Results. The stellar parameters for 34 OB stars within the core of NGC 346 are derived and presented here. By the use of both He <ns0:sc>I</ns0:sc> and He <ns0:sc>II</ns0:sc> lines, the partial degeneracy between the stellar parameters of effective surface temperature, surface gravity, and projected rotational velocity is overcome. A lower limit to the binary fraction of the sample of stars is found to be at least 46%. Conclusions. Based on comparisons with analysis conducted on an overlapping sample of stars within NGC 346, the Bayesian statistic technique approach is shown to be a viable method to measure stellar parameters for hot massive stars in low-metallicity environments even when only low-resolution spectra are available.