maravelias.info

A proceedings paper from IAUS 366 that took place virtually back in October 2021 (for which I had another poster contribution) was finally published at the end of 2022. It summarizes a collective work led by Michaela on B[e] Supergiants and Yellow Hypergiants, two massive star phases where we observe episodic mass loss.

Environments of evolved massive stars: evidence for episodic mass ejections

M. Kraus, L. S. Cidale, M. L. Arias, A. F. Torres, I. Kolka, G. Maravelias, D. H. Nickeler, W. Glatzel and T. Liimets

The post-main sequence evolutionary path of massive stars comprises various transition phases, in which the stars shed large amounts of material into their environments. Our studies focus on two of them: B[e] supergiants and yellow hypergiants, for which we investigate the structure and dynamics within their environments. We find that each B[e] supergiant is surrounded by a unique set of rings or arc-like structures. These structures are either stable over time or they display high variability, including expansion and dilution. In contrast, yellow hypergiants are embedded in multiple shells of gas and dust. These objects are famous for their outburst activity. Moreover, the dynamics in their extended atmospheres imply an enhanced pulsation activity prior to outburst. The physical mechanism(s) leading to episodic mass ejections in these two types of stars is still uncertain. We propose that strange-mode instabilities, excited in the inflated envelopes of these objects, play a significant role.

IAUS 366, 2022 (NASA/ADS link)

This is the first paper that results from my work with the ASSESS team over the last years. It focuses on the development of a machine-learning photometric classifier to characterize massive stars originating from IR (Spitzer) catalogs, which will help us understand the episodic mass loss. The first paper presents the method and the multiple test we performed to understand its capabilities and limitations. Now we proceed with the derivation of the catalogs and their analysis.

A machine-learning photometric classifier for massive stars in nearby galaxies I. The method

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

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 the models while 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 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.
Methods. 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 which helps with foreground source detection. We grouped them in 7 classes (Blue, Red, Yellow, B[e] supergiants, Luminous Blue Variables, Wolf-Rayet, and outliers, e.g. QSOs and background galaxies). As this training set is highly imbalanced, we implemented synthetic data generation to populate the underrepresented classes and improve separation by undersampling the majority class. We built an ensemble classifier utilizing color indices as features. The probabilities from three machine-learning algorithms (Support Vector Classification, Random Forests, Multi-layer 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 sample 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, Sextans A) we obtained an overall accuracy of ∼ 70%. This discrepancy is attributed to the different metallicity and extinction effects of their host galaxies. Motivated by the presence of missing values we investigated the impact of missing data imputation using simple replacement with mean values and an iterative imputor, which proved to be more capable. We also investigated the feature importance to find that r − i and y − [3.6] were 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 sampling 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.

arXiv: 2203.08125