de Wit et al. 2025: Investigating episodic mass loss in evolved massive stars: III. Spectroscopy of dusty massive stars in three northern galaxies

S. de Wit, G. Muñoz-Sanchez, G. Maravelias, A.Z. Bonanos, K. Antoniadis, D. García-Álvarez, N. Britavskiy, A. Ruiz, A. Philippopoulou Mass loss in massive stars is crucial to understanding how these stars evolve and explode. Despite increasing evidence indicating its importance, episodic mass loss remains poorly understood. Here we report the …

Maravelias et al. 2025: A machine-learning photometric classifier for massive stars in nearby galaxies II. The catalog

G. Maravelias, A. Z. Bonanos, K. Antoniadis, G. Munoz-Sanchez, E. Christodoulou, S. de Wit, E. Zapartas, K. Kovlakas, F. Tramper, P. Bonfini, S. Avgousti Mass loss is a key aspect of stellar evolution, particularly in evolved massive stars, yet episodic mass loss remains poorly understood. To investigate this, we need …

ASSESS meets Alex de Koter, Emily Levesque, and Henry Lamers

As we are actively working on a series of forthcoming papers, we are meticulously refining the final stages of data analysis and interpretation. Especially when encountering new and exciting results we must be careful to not overlook any potential issues. While our expertise in Red Supergiants is well-established, seeking additional …

Kyritsis et al. 2021: A new automated tool for the spectral classification of OB stars

A new automated tool for the spectral classification of OB stars E. Kyritsis, G. Maravelias, A. Zezas, P. Bonfini, K. Kovlakas, P. Reig (abridged) We develop a tool for the automated spectral classification of OB stars according to their sub-types. We use the regular Random Forest (RF) algorithm, the Probabilistic …