We are now in an era of large spectroscopic surveys of OB-type stars. Quantitative spectroscopic analysis of these modern datasets is enabling us to review the physical properties of blue massive stars with robust samples, not only revisiting mean properties and general trends, but also incorporating information about the effects of second-order parameters. We investigate the spectral type -- effective temperature (SpT - Teff) calibration for O-type dwarfs, and its claimed dependence on metallicity, using statistically-meaningful samples of stars extracted from the IACOB and VFTS surveys. We perform a homogeneous differential spectroscopic analysis of 33 Galactic and 53 LMC O dwarfs (spanning spectral types of O4 - O9.7) using the IACOB-GBAT package, a chi-square-fitting algorithm based on a large pre-computed grid of FASTWIND models, and standard techniques for the hydrogen/helium analysis of O-type stars. We compare the estimated effective temperatures and gravities as a function of (internally consistent) spectral classifications. While the general trend is that the temperature of a star increases with earlier spectral types and decreasing metallicity, we show that the large range of gravities found for O-type dwarfs -- spaning up to 0.45-0.50 dex in some spectral bins -- plays a critical role on the dependence of the effective temperature calibrations as a function of spectral type and metallicity. This result warns us about the use of SpT - Teff calibrations for O-dwarfs which ignore the effects of gravity, and highlights the risks of employing calibrations based on small samples. The effects of this scatter in gravities (evolutionary status) for O-type dwarfs should be included in future recipes which employ SpT - Teff calibrations.
It may interest you
-
The amount and complexity of data delivered by modern galaxy surveys has been steadily increasing over the past years. New facilities will soon provide imaging and spectra of hundreds of millions of galaxies. Extracting coherent scientific information from these large and multi-modal data sets remains an open issue for the community and data-driven approaches such as deep learning have rapidly emerged as a potentially powerful solution to some long lasting challenges. This enthusiasm is reflected in an unprecedented exponential growth of publications using neural networks, which have gone
Advertised on -
Stellar ages are key to several fields of astrophysics such as exoplanet research, galactic-archeology, and of course stellar physics. Obtaining the ages of stars is however not straightforward and requires stellar modeling. The most widely used technique only requires stellar colors or temperature and surface gravity, but the uncertainties are quite large. This technique is most efficient for stars belonging to clusters, as they were born from the same molecular cloud and share the same ages. In the last decades, based on the study of stellar acoustic waves, asteroseismology became the most
Advertised on -
In the 90s, the COBE satellite discovered that not all the microwave emission from our Galaxy behaved as expected. Part of this signal was later assigned to a fresh new emission component, spatially correlated with the Galactic dust emission, which showed greater importance in the microwave range of frequencies. It has been named since as “anomalous microwave emission”, or AME. The current main hypothesis to explain the AME origin is that it is emitted by small dust particles which undergo fast spinning movements. In Fernández-Torreiro et al. (2023), we study the observational properties of
Advertised on