ORCID Profile
0000-0001-8631-7700
Current Organisation
KU Leuven
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Publisher: Cambridge University Press (CUP)
Date: 08-2012
DOI: 10.1017/S1743921314004657
Abstract: Recent studies have claimed the existence of very massive stars (VMS) up to 300 M ⊙ in the local Universe. As this finding may represent a paradigm shift for the canonical stellar upper-mass limit of 150 M ⊙ , it is timely to discuss the status of the data, as well as the far-reaching implications of such objects. We held a Joint Discussion at the General Assembly in Beijing to discuss (i) the determination of the current masses of the most massive stars, (ii) the formation of VMS, (iii) their mass loss, and (iv) their evolution and final fate. The prime aim was to reach broad consensus between observers and theorists on how to identify and quantify the dominant physical processes.
Publisher: EDP Sciences
Date: 09-2019
DOI: 10.1051/0004-6361/201935916
Abstract: We present a clean, magnitude-limited (IRAC1 or WISE1 ≤ 15.0 mag) multiwavelength source catalog for the Small Magellanic Cloud (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, retrieved from SEIP, VMC, IRSF, AKARI, HERITAGE, Gaia , SkyMapper, NSC, Massey (2002, ApJS, 141, 81), and GALEX, ranging from the ultraviolet to the far-infrared. Additionally, radial velocities and spectral classifications were collected from the literature, and infrared and optical variability statistics were retrieved from WISE, SAGE-Var, VMC, IRSF, Gaia , NSC, and OGLE. The catalog was 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 allowed us to remove the foreground contamination. We estimate that about 99.5% of the targets in our catalog are most likely genuine members of the SMC. Using the evolutionary tracks and synthetic photometry from MESA Isochrones & Stellar Tracks and the theoretical J − K S color cuts, we identified 1405 red supergiant (RSG), 217 yellow supergiant, and 1369 blue supergiant candidates in the SMC in five different color-magnitude diagrams (CMDs), where attention should also be paid to the incompleteness of our s le. 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 mass for the RSG population may be as low as 7 or even 6 M ⊙ and that the RSG is well separated from the asymptotic giant branch (AGB) population even at faint magnitude, 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 AGB stars. We encourage the interested reader to further exploit the potential of our catalog.
Publisher: EDP Sciences
Date: 10-2022
DOI: 10.1051/0004-6361/202141397
Abstract: Context. 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. Moreover, episodic mass loss in evolved massive stars is not included in models, and the importance of its role in the evolution of massive stars is currently undetermined. Aims. A major hindrance to determining the role of episodic mass loss is the lack of large s les 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. Methods. We compiled a large catalog of known massive stars in M 31 and M 33 using IR ( Spitzer ) and optical (Pan-STARRS) photometry, as well as Gaia astrometric information, which helps with foreground source detection. We grouped them into seven classes (Blue, Red, Yellow, B[e] supergiants, luminous blue variables, Wolf-Rayet stars, and outliers, e.g., quasi-stellar objects and background galaxies). As this training set is highly imbalanced, we implemented synthetic data generation to populate the underrepresented classes and improve separation by unders ling the majority class. We built an ensemble classifier utilizing color indices as features. The probabilities from three machine-learning algorithms (Support Vector Classification, Random Forest, and Multilayer Perceptron) were combined to obtain the final classification. Results. The overall weighted balanced accuracy of the classifier is ∼83%. Red supergiants are always recovered at ∼94%. Blue and Yellow supergiants, B[e] supergiants, and background galaxies achieve ∼50 − 80%. Wolf-Rayet sources are detected at ∼45%, while luminous blue variables are recovered at ∼30% from one method mainly. This is primarily due to the small s le sizes of these classes. In addition, the mixing of spectral types, as there are no strict boundaries in the features space (color indices) between those classes, complicates the classification. In an independent application of the classifier to other galaxies (IC 1613, WLM, and Sextans A), we obtained an overall accuracy of ∼70%. This discrepancy is attributed to the different metallicity and extinction effects of the host galaxies. Motivated by the presence of missing values, we investigated the impact of missing data imputation using a simple replacement with mean values and an iterative imputer, which proved to be more capable. We also investigated the feature importance to find that r − i and y − [3.6] are the most important, although different classes are sensitive to different features (with potential improvement with additional features). Conclusions. The prediction capability of the classifier is limited by the available number of sources per class (which corresponds to the s ling of their feature space), 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 as well as at lower metallicities (with some accuracy loss), making it an excellent tool for accentuating interesting objects and prioritizing targets for observations.
Publisher: EDP Sciences
Date: 07-2023
DOI: 10.1051/0004-6361/202245650
Abstract: Observations of in idual massive stars, super-luminous supernovae, gamma-ray bursts, and gravitational wave events involving spectacular black hole mergers indicate that the low-metallicity Universe is fundamentally different from our own Galaxy. Many transient phenomena will remain enigmatic until we achieve a firm understanding of the physics and evolution of massive stars at low metallicity ( Z ). The Hubble Space Telescope has devoted 500 orbits to observing ∼250 massive stars at low Z in the ultraviolet (UV) with the COS and STIS spectrographs under the ULLYSES programme. The complementary X-Shooting ULLYSES (XShootU) project provides an enhanced legacy value with high-quality optical and near-infrared spectra obtained with the wide-wavelength coverage X-shooter spectrograph at ESO’s Very Large Telescope. We present an overview of the XShootU project, showing that combining ULLYSES UV and XShootU optical spectra is critical for the uniform determination of stellar parameters such as effective temperature, surface gravity, luminosity, and abundances, as well as wind properties such as mass-loss rates as a function of Z . As uncertainties in stellar and wind parameters percolate into many adjacent areas of astrophysics, the data and modelling of the XShootU project is expected to be a game changer for our physical understanding of massive stars at low Z . To be able to confidently interpret James Webb Space Telescope spectra of the first stellar generations, the in idual spectra of low- Z stars need to be understood, which is exactly where XShootU can deliver.
No related grants have been discovered for Frank Tramper.