Archive for September, 2022

New Paper: Properties of luminous red supergiant stars in the Magellanic Clouds

Posted September 26, 2022 By grigoris

Properties of luminous red supergiant stars in the Magellanic Clouds

S. de Wit, A.Z. Bonanos, F. Tramper, M. Yang, G. Maravelias, K. Boutsia, N. Britavskiy, and E. Zapartas

There is evidence that some red supergiants (RSGs) experience short lived phases of extreme mass loss, producing copious amounts of dust. These episodic outburst phases help to strip the hydrogen envelope of evolved massive stars, drastically affecting their evolution. However, to date, the observational data of episodic mass loss is limited. This paper aims to derive surface properties of a spectroscopic sample of fourteen dusty sources in the Magellanic Clouds using the Baade telescope. These properties may be used for future spectral energy distribution fitting studies to measure the mass loss rates from present circumstellar dust expelled from the star through outbursts. We apply MARCS models to obtain the effective temperature (Teff) and extinction (AV) from the optical TiO bands. We use a χ2 routine to determine the best fit model to the obtained spectra. We compute the Teff using empirical photometric relations and compare this to our modelled Teff. We have identified a new yellow supergiant and spectroscopically confirmed eight new RSGs and one bright giant in the Magellanic Clouds. Additionally, we observed a supergiant B[e] star and found that the spectral type has changed compared to previous classifications, confirming that the spectral type is variable over decades. For the RSGs, we obtained the surface and global properties, as well as the extinction AV. Our method has picked up eight new, luminous RSGs. Despite selecting dusty RSGs, we find values for AV that are not as high as expected given the circumstellar extinction of these evolved stars. The most remarkable object from the sample, LMC3, is an extremely massive and luminous evolved massive star and may be grouped amongst the largest and most luminous RSGs known in the Large Magellanic Cloud (log(L∗/L⊙)∼5.5 and R=1400 R⊙).

Fig. 9: Top: HRD indicating the locations of our LMC targets with inverted red triangles. The Teff for all data points was derived through the TiO method. Smaller light grey squares and stars are objects from Levesque et al. (2006) and Davies et al. (2013), respectively. For the two outliers we have extended the uncertainty assuming a shift of 0.3 mag in the K−band (dotted vertical error bar) instead of only the propagated uncertainty, to visualize the effect of intrinsic variability. The colour map represents the central 12C mass fraction, while the nodes on the track again indicate a step of 104 years

arXiv: 2209.11239

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New paper: Using machine learning to investigate the populations of dusty evolved stars in various metallicities

Posted September 19, 2022 By grigoris

This is actually a preview of what will follow after the first paper of the machine-learning classifier. We put it into action to get predictions for a number of galaxies and we start exploring the results. Of more interest is the fractions of the populations with metallicity, although a more detailed study is needed to take care of all caveats.

Using machine learning to investigate the populations of dusty evolved stars in various metallicities

Grigoris Maravelias, Alceste Z. Bonanos, Frank Tramper, Stephan de Wit, Ming Yang, Paolo Bonfini, Emmanuel Zapartas, Konstantinos Antoniadis, Evangelia Christodoulou, Gonzalo Muñoz-Sanchez

Mass loss is a key property to understand stellar evolution and in particular for low-metallicity environments. Our knowledge has improved dramatically over the last decades both for single and binary evolutionary models. However, episodic mass loss although definitely present observationally, is not included in the models, while its role is currently undetermined. A major hindrance is the lack of large enough samples of classified stars. We attempted to address this by applying an ensemble machine-learning approach using color indices (from IR/Spitzer and optical/Pan-STARRS photometry) as features and combining the probabilities from three different algorithms. We trained on M31 and M33 sources with known spectral classification, which we grouped into Blue/Yellow/Red/B[e] Supergiants, Luminous Blue Variables, classical Wolf-Rayet and background galaxies/AGNs. We then applied the classifier to about one million Spitzer point sources from 25 nearby galaxies, spanning a range of metallicites (1/15 to ∼3 Z⊙). Equipped with spectral classifications we investigated the occurrence of these populations with metallicity.

The fractions, of the predicted class members over the total sample size for each galaxy, with metallicity.

arXiv: 2209.06303

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