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

Red dwarfs are the most common stars in the galaxy. In recent years they have become key targets in the search for exoplanets. These stars are usually accompanied by rocky planets and due to their low brightness, their habitable zone is close to the star, making it easier to find planets that are within it. GJ 1002 is a red dwarf just one-eighth the mass of the Sun, located only 15.8 light-years away. Using radial velocity measurements from the ESPRESSO and CARMENES spectrographs, we have discovered the presence of two Earth-like and potentially habitable planets. The planets, GJ 1002 b and