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

It is well known that fullerenes – big, complex, and highly resistant carbon molecules with potential applications in nanotechnology – are mostly seen in planetary nebulae (PNe); old dying stars with progenitor masses similar to our Sun. Fullerenes, like C60 and C70, have been detected in PNe whose infrared (IR) spectra are dominated by broad unidentified IR (UIR) plateau emissions. The identification of the chemical species (structure and composition) responsible for such UIR emission widely present in the Universe is a mystery in astrochemistry; although they are believed to be carbon-rich

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