Javier Gorosabel award on pro-am collaborations

In the latest meeting of the Spanish Astronomical Society (5-9 September, Tenerife, Spain) it was announced that our work with Emmanuel (Manos) Kardasis on Venus’ Cloud Discontinuity (published at the beginning of 2022, in the journal Atmosphere) was the winner for the 2nd iteration of the Javier Gorosabel Award on …

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 …

A summer solstice remote presentation for TLS Tautenburg

In March I got invited to give a talk for the Thüringer Landessternwarte Tautenburg group. Back then June 21 looked like a far date in a relative relaxed time period. I was so wrong… they are so hectic days! Nevertheless, I managed to prepare a talk entitled: “The ASSESS classifier: …

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 …