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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⊙).

arXiv: 2209.11239

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.

arXiv: 2209.06303

Three poster contributions during EAS 2021 with the following … statistics: all of them on massive stars,  two within the framework of the ASSESS project, and two on machine-learning applications.

1. Applying machine-learning methods to build a photometric classifier for massive stars in nearby galaxies

Grigoris Maravelias, Alceste Bonanos, Frank Tramper, Stephan de Wit, Ming Yang, Paolo Bonfini

Mass loss is a key parameter in the evolution of massive stars. Despite the recent progress in the theoretical understanding of how stars lose mass, discrepancies between theory and observations still hold. Even worse, episodic mass loss in evolved massive stars is not included in the models while the importance of its role in the evolution os massive stars is currently undetermined. A major hindrance to determining the role of episodic mass loss is the lack of large samples of classified stars. Given the recent availability of extensive photometric catalogs from various surveys spanning a range of metallicity environments, we aim to remedy the situation by applying machine learning techniques to these catalogs.We compiled a large catalog of known massive stars in M31 and M33, using IR (Spitzer) and optical (Pan-STARRS) photometry, as well as Gaia astrometric information. We grouped them in 7 classes (Blue, Red, Yellow, B[e] supergiants, Luminous Blue Variables, Wolf-Rayet, and outliers, e.g. QSO’s and background galaxies). Using this catalog as a training set, we built an ensemble classifier utilizing color indices as features. The probabilities from three machine-learning algorithms (Support Vector Classification, Random Forests, Neural Networks) are combined to obtain the final classifications. The overall performance of the classifier is ~87%. Highly populated (Red/Blue/Yellow Supergiants) and well-defined classes (B[e] Supergiants) have a high recovery rate between ~98-74%. On the contrary, Wolf-Rayet sources are detected at ~20% while Luminous Blue Variables are almost non-existent. The is mainly due to the small sample sizes of these classes, although M31 and M33 have spectral classifications for several massive stars (about 2500). In addition, the mixing of spectral types, as there are no strict boundaries in the features space (color indexes) between those classes, complicates the classification. In an independent application of the classifier to other galaxies (IC 1613, WLM, Sextans A) we obtained an overall accuracy of ~71% despite the missing values on their features (which we replace with averaged values from the training sample). This approach results only in a few percent difference, with the remaining discrepancy attributed to the different metallicity environments of their host galaxies. The classifier’s prediction capability is only limited by the available number of sources per class, reflecting the rarity of these objects and the possible physical links between these massive star phases. Our methodology is also efficient in correctly classifying sources with missing data and at lower metallicities, making it an excellent tool for spotting interesting objects and prioritizing targets for observations. Future spectroscopic observations will offer a test-bed of its actual performance along with opportunities for improvement.

For more see this k-poster (submitted for SS32: Machine Learning and Visualisation in Data Intensive Era ).

2. A new automated tool for the spectral classification of OB stars

E. Kyritsis, G. Maravelias, A. Zezas, P. Bonfini, K. Kovlakas, P. Reig

As more and more large spectroscopic surveys become available, an automated approach in spectral classification becomes necessary. Due to the importance of the massive stars it is of paramount importance to identify the phenomenological parameters of these stars (e.g., the spectral type ) which can be used as proxies to their physical parameters (e.g mass, temperature).
In this work, we use the Random Forest (RF) algorithm to develop a tool for automated spectral classification of the OB-type stars into their sub-types. We use the regular RF algorithm, the Probabilistic RF (PRF) which is an extension of RF that incorporates uncertainties, and we introduce the KDE – RF method which is a combination of the Kernel-Density Estimation and the RF algorithm. We train the algorithms on the Equivalent Width (EW) of characteristic absorption lines measured in the spectra from large Galactic (LAMOST, GOSSS) and extragalactic surveys (2dF, VFTS) with available spectral-type classification. By following an adaptive binning approach we group the labels of these data on 11 sub-types within the range O3-B9. We examined which of the characteristic spectral lines (features) are more important to use based on a number of feature selection methods and we searched for the optimal hyper-parameters of the classifiers, to achieve the best performance.
From the feature screening process, we find 13 spectral lines as the optimal number of features. We find that the overall accuracy score is ~ 76 % with similar results across all approaches, with our KDE – RF being slightly lower at ~ 73 %. In addition, we show that our optimized RF model can reach an overall accuracy score of ~ 85 % in the ideal case of robust measurement of the weakest characteristic spectral lines. We apply our model in other observational data sets providing examples of potential application of our classifier on real science cases. We find that it performs well for both single massive stars and for the companion massive stars in Be X-ray Binaries, especially for data with S/N in the range 50-300. Furthermore, we present an alternative model for lower quality data S/N < 25 based on a reduced feature-set classification scheme, including only the strongest spectral lines.
The similarity in the performances of our models indicates the robustness and the reliability of the RF algorithm when used for spectral classification of early-type stars. This is strengthened also by the fact that we are working with real-world data and not with simulations. In addition, the approach presented in this work is very fast and applicable to products from different surveys in terms of quality (e.g different resolutions) and of different formats (e.g., absolute or normalized flux).

For more see this k-poster (submitted for S16: Massive stars: birth, rotation, and chemical evolution).

3. Evolved massive stars in the Magellanic Clouds

Ming Yang, Alceste Bonanos, Biwei Jiang, Jian Gao, Panagiotis Gavras, Grigoris Maravelias, Man I Lam, Shu Wang, Xiaodian Chen, Yi Ren, Frank Tramper, Zoi Spetsieri

We present two clean, magnitude-limited (IRAC1 or WISE1≤15.0 mag) multiwavelength source catalogs for the Large and Small Magellanic Cloud (LMC and SMC). The catalogs were built by crossmatching (1”) and deblending (3”) between the source list of Spitzer Enhanced Imaging Products (SEIP) and Gaia Data Release 2 (DR2), with strict constraints on the Gaia astrometric solution in order to remove the foreground contamination. It is estimated that about 99.5% of the targets in our catalog are most likely genuine members of the LMC and SMC. The LMC catalog contains 197,004 targets in 52 different bands, while SMC catalog including contains 45,466 targets in 50 different bands, ranging from the ultraviolet to the far-infrared. Additional information about radial velocities and spectral and photometric classifications were collected from the literature. For the LMC, we compare our sample with the sample from Gaia Collaboration et al. (2018), indicating that the bright end of our sample is mostly comprised of blue helium-burning stars (BHeBs) and red HeBs with inevitable contamination of main sequence stars at the blue end. For the SMC, by using the evolutionary tracks and synthetic photometry from MESA Isochrones & Stellar Tracks and the theoretical J-Ks color cuts, we identified and ranked 1,405 red supergiant (RSG), 217 yellow supergiant (YSG), and 1,369 blue supergiant (BSG) candidates in the SMC in five different color-magnitude diagrams (CMDs), where attention should also be paid to the incompleteness of our sample. For the LMC, due to the problems with models, we applied modified magnitude and color cuts based on previous studies, and identified and ranked 2,974 RSG, 508 YSG, and 4,786 BSG candidates in the LMC in six CMDs. The comparison between the CMDs from the two catalogs of the LMC SMC indicates that the most distinct difference appears at the bright red end of the optical and near-infrared CMDs, where the cool evolved stars (e.g., RSGs, asymptotic giant branch stars, and red giant stars) are located, which is likely due to the effect of metallicity and star formation history. A further quantitative comparison of colors of massive star candidates in equal absolute magnitude bins suggests that there is essentially no difference for the BSG candidates, but a large discrepancy for the RSG candidates since LMC targets are redder than the SMC ones, which may be due to the combined effect of metallicity on both spectral type and mass-loss rate as well as the age effect. The effective temperatures (Teff) of massive star populations are also derived from reddening-free color of (J-Ks). The Teff ranges are 3500≤Teff≤5000 K for an RSG population, 5000≤Teff≤8000 K for a YSG population, and Teff≥8000 K for a BSG population, with larger uncertainties toward the hotter stars.

For more see this k-poster (submitted for S16: Massive stars: birth, rotation, and chemical evolution).

Evolved Massive Stars at Low-metallicity IV.Using 1.6μm “H-bump” to identify red supergiant stars:a case study of NGC 6822

Ming Yang, Alceste Z. Bonanos, Biwei Jiang, Man I Lam, Jian Gao, Panagiotis Gavras, Grigoris Maravelias, Shu Wang, Xiao-Dian Chen, Frank Tramper, Yi Ren, Zoi T. Spetsieri

Abstract

Although the summer has finished long ago, only now I got some time to update on summer activities, i.e. a number of conferences I attended and contributed to.

Since 2018, I have been working in an automated classifier for massive stars in nearby galaxies, using photometric datasets. These have been produced by my colleagues within the ASSESS team, an ERC project led by Alceste Bonanos at the National Observatory of Athens, and I have been responsible to develop a machine learning method to achieve this. We have made a lot of progress and we have reached to the point that the results are almost final (working now on the Maravelias et al. paper). So, this work has been presented in:

1. A poster presentation at the Supernova Remnants II, Chania, Greece, 3-8 June 2019,
   as “Identifying massive stars in nearby galaxies, in a smart way”
2. A talk, done by Frank Tramper due to my unavailability to attend the 14th Hellenic Astronomical Conference, Volos, Greece, 8-11 July 2019,
   as “Automated classification of massive stars in nearby galaxies”
3. A talk at the Computational Intelligence in Remote Sensing and Astrophysics, FORTH workshop, Heraklion, Greece, 17-19 July 2019,
   as “An automated classifier of massive stars in nearby galaxies”
4. A remote talk for the ASTROSTAT 2nd Consortium meeting, Boston, USA, 18-19 July 2019,
   as “Towards an automated classifier of massive stars in nearby galaxies”

Grigoris Maravelias, Alceste Z. Bonanos, Ming Yang, Frank Tramper, Stephan A. S. de Wit, Paolo Bonfini

Abstract:
Current photometric surveys can provide us with multiwavelength measurements for a vast numbers of stars in many nearby galaxies. Although the majority of these stars are evolved luminous stars (e.g. Wolf-Rayet, Blue/Yellow/Red Supergiants), we lack an accurate spectral classification, due to the demands that spectroscopy faces at these distances and for this number of stars. What we can do instead is to take advantage of machine learning algorithms (such as Support Vector Machines, Random Forests, Convolutional Neural Networks) to build an automated classifier based on a large multi-wavelength photometric catalog. We have compiled such a catalog based on optical (e.g. Pan-STARRS, OGLE) and IR (e.g. 2MASS, Spitzer) surveys, combined with astrometric information from the GAIA mission. We have also gathered spectroscopic samples of massive stars for a number of nearby galaxies (e.g. the Magellanic Clouds, M31, M33) and by using our algorithm we have achieved a success ratio of more than 80% for the training and test samples. By applying the fully trained algorithm to the available photometric datasets, we can uncover previously unclassified sources, which will become our prime candidates for spectroscopic follow-up aiming to confirm their nature and our approach.

Also Ming has presented his work in a couple of conferences:

1. As a poster presentation at the Supernova Remnants II, Chania, Greece, 3-8 June 2019,

“Evolved Massive Stars at Low-metallicity: A Source Catalog for the Small Magellanic Cloud”

Ming Yang, Alceste Z. Bonanos, Bi-Wei Jiang, Jian Gao, Panagiotis Gavras, Grigoris Maravelias, Yi Ren, Shu Wang, Meng-Yao Xue, Frank Tramper, Zoi T. Spetsieri, Ektoras Pouliasis, Stephan A. S. de Wit

We present a clean, magnitude-limited (IRAC1 or WISE1 ≤ 15.0 mag) multiwavelength source catalog for the SMC with 45,466 targets in total, with the purpose of building an anchor for future studies, especially for the massive star populations at low-metallicity. The catalog contains data in 50 different bands including 21 optical and 29 infrared bands, ranging from the ultraviolet to the far-infrared. Additionally, radial velocities and spectral classifications were collected from the literature, as well as infrared and optical variability statistics were retrieved from different datasets. The catalog was essentially built upon a 1′′ crossmatching and a 3′′ deblending between the SEIP source list and Gaia DR2 photometric data. Further constraints on the proper motions and parallaxes from Gaia DR2 allowed us to remove the foreground contamination. We estimated that about 99.5% of the targets in our catalog were most likely genuine members of the SMC. By using the evolutionary tracks and synthetic photometry from MIST and the theoretical J−Ks color cuts, we identified 1,405 RSG, 217 YSG and 1,369 BSG candidates in the SMC in five different CMDs. We ranked the candidates based on the intersection of different CMDs. A comparison between the models and observational data shows that the lower limit of initial masses for the RSGs population may be as low as 7 or even 6 M⊙, making RSGs a unique population connecting the evolved massive and intermediate stars, since stars with initial mass around 6 to 8 M⊙ are thought to go through a second dredge-up to become AGBs. We encourage the interested reader to further exploit the potential of our catalog.

2. As a talk at the ESO workshop “A synoptic view of the Magellanic Clouds: VMC, Gaia and beyond”, Garching near Munich, Germany, September 9-13, 2019

“Evolved Massive Stars and Red Supergiant Stars in the Magellanic Clouds”

Ming Yang, Alceste Z. Bonanos, Bi-Wei Jiang, Jian Gao, Panagiotis Gavras, Grigoris Maravelias, Yi Ren, Shu Wang, Meng-Yao Xue, Frank Tramper, Zoi T. Spetsieri, Ektoras Pouliasis, and Stephan de Wit

We present an ongoing investigation of infrared properties, variabilities, and mass loss rate (MLR) of evolved massive stars in the Magellanic Clouds, especially the red supergiant stars (RSGs). For the LMC, 744 RSGs compiled from the literature are identified and analysed by using the color-magnitude diagram (CMD), spectral energy distribution (SED) and mid-infrared (MIR) variability, based on 12 bands of near-infrared (NIR) to MIR co-added data from 2MASS, Spitzer and WISE, and ∼6.6 yr of MIR time-series data collected by the ALLWISE and NEOWISE-R projects. The results show that there is a relatively tight and positive correlation between the brightness, MIR variability, MLR, and the warm dust or continuum, where both the variability and the luminosity may be important for the MLR. The identified RSG sample has been compared with the theoretical evolutionary models and shown that the discrepancy between observation and evolutionary models can be mitigated by considering both variability and extinction. For the SMC, we present a relatively clean, magnitude-limited (IRAC1 or WISE1 ≤ 15.0 mag) multiwavelength source catalog with 45,466 targets in total, intending to build an anchor for the future studies, especially the massive stars at low-metallicity. It contains data in 50 different bands including 21 optical and 29 infrared bands, retrieved from SEIP, VMC, IRSF, AKARI, Heritage, Gaia, SkyMapper, NSC, Massey et al. (2002), and GALEX, ranging from the ultraviolet to the far-infrared. Additionally, radial velocities and spectral classifications are collected from the literature, as well as the infrared and optical variability information derived from WISE, SAGE-Var, VMC, IRSF, Gaia, NSC, and OGLE. The catalog is essentially built upon a 1” crossmatching and a 3” deblending between the Spitzer Enhanced Imaging Products (SEIP) source list and Gaia Data Release 2 (DR2) photometric data. Further constraints on the proper motions and parallaxes from Gaia DR2 allow us to remove the foreground contamination. We estimate that about 99.5% of the targets in our catalog are likely to be the genuine members of the SMC. By using the evolutionary tracks and synthetic photometry from MESA Isochrones & Stellar Tracks and the theoretical J−Ks color cuts, we identify 1,405 red supergiant, 217 yellow supergiant and 1,369 blue supergiant candidates in the SMC in five different CMDs. We rank the candidates based on the intersection of the different CMDs. A comparison between the models and observational data shows that, the lower limit of the RSGs population may reach to 7 or even 6M⊙, making RSGs an unique population connecting the evolved massive and intermediate stars, since stars with initial mass around 6 to 8M⊙ are thought to go through a second dredge-up to become asymptotic giant branch stars. We encourage the interested reader to further exploit the potential of our catalog, including, but not limited to, massive stars, supernova progenitors, star formation history and stellar population. Detailed analysis and comparison of RSGs in the LMC and SMC may be also presented depending on the progress of the investigation.