about Grigoris Maravelias
Author: <span>grigoris</span>

Author: grigoris

New Paper: Properties of luminous red supergiant stars in the Magellanic Clouds

Properties of luminous red supergiant stars in the Magellanic Clouds S. de Wit, A.Z. Bonanos, F. Tramper, M. Yang, G. Maravelias, K. Boutsia, N. Britavskiy, and E. Zapartas There is evidence that some red supergiants (RSGs) experience short lived phases of extreme mass loss, producing copious amounts of dust. These …

New paper: Using machine learning to investigate the populations of dusty evolved stars in various metallicities

This is actually a preview of what will follow after the first paper of the machine-learning classifier. We put it into action to get predictions for a number of galaxies and we start exploring the results. Of more interest is the fractions of the populations with metallicity, although a more …

The Astrostatistics Summer School 2022 in Crete – the announcement

Following the success of the first Astrostatistics Summer School in Crete in 2019 (18-21 June), we now organize its second iteration (11-15 July). Initially scheduled for 2020, but we all now what happened and it was delayed. Now things to have matured so that we can actually repeat it and …

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

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 …

New Paper: Amateur Observers Witness the Return of Venus’ Cloud Discontinuity

The following paper is the result of a tedious task that my good friend Manos Kardasis undertook over the last two+ years. He noticed the presence of this (relatively newly discovered) feature in Venus and collected images from amateur observers worldwide to study in detail the discontinuity and constrain some …