Maravelias et al. 2022: A machine-learning photometric classifier for massive stars in nearby galaxies I. The method

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 …

Observing time granted!

Around Christmas of 2021 Santa brought us two gifts, one just before the holidays and another … just after! We are definitely not disappointed! Two of our proposals have been granted observing time (while another one at ESO didn’t make it this time). We got 4.2 h with FORS2 multislit …

Maravelias & Kraus 2022: Bouncing against the Yellow Void — exploring the outbursts of ρ Cas from visual observations

Bouncing against the Yellow Void — exploring the outbursts of ρ Cas from visual observations Grigoris Maravelias and Michaela Kraus Massive stars are rare but of paramount importance for their immediate environment and their host galaxies. They lose mass from their birth through strong stellar winds up to their spectacular …

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 …

ASSESS Team on IAUS 366 on the Origin of Outflows in Evolved Stars

The ASSESS team is joining the IAUS 366 “On the Origin of Outflows in Evolved Stars”, organized virtually from Nov 1-5. Alceste will give an overview (Wednesday, Nov. 3, @12:20 UTC) of the ASSESS project followed by the advance we have achieved over the last years, as first results are …