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:
- 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”
- 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”
- 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”
- 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
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:
“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.