The amount and complexity of data delivered by modern galaxy surveys has been steadily increasing over the past years. New facilities will soon provide imaging and spectra of hundreds of millions of galaxies. Extracting coherent scientific information from these large and multi-modal data sets remains an open issue for the community and data-driven approaches such as deep learning have rapidly emerged as a potentially powerful solution to some long lasting challenges. This enthusiasm is reflected in an unprecedented exponential growth of publications using neural networks, which have gone

Stellar ages are key to several fields of astrophysics such as exoplanet research, galactic-archeology, and of course stellar physics. Obtaining the ages of stars is however not straightforward and requires stellar modeling. The most widely used technique only requires stellar colors or temperature and surface gravity, but the uncertainties are quite large. This technique is most efficient for stars belonging to clusters, as they were born from the same molecular cloud and share the same ages. In the last decades, based on the study of stellar acoustic waves, asteroseismology became the most

In the 90s, the COBE satellite discovered that not all the microwave emission from our Galaxy behaved as expected. Part of this signal was later assigned to a fresh new emission component, spatially correlated with the Galactic dust emission, which showed greater importance in the microwave range of frequencies. It has been named since as “anomalous microwave emission”, or AME. The current main hypothesis to explain the AME origin is that it is emitted by small dust particles which undergo fast spinning movements. In Fernández-Torreiro et al. (2023), we study the observational properties of