Context. The intracluster light (ICL) comprises stars that are not bound to individual galaxies within a galaxy cluster, and it provides insights into the cluster mass distribution, evolutionary history, and dynamical state. Aims. We study the viability of the intracluster stellar mass as a proxy for computing the total mass profiles of galaxy

Context. Recent observational results from asteroseismic studies show that an important fraction of solar-like stars do not present detectable stochastically excited acoustic oscillations. This non-detectability seems to correlate with a high rotation rate in the convective envelope and a high surface magnetic activity. At the same time, the

SUNRISE III is a balloon-borne solar observatory dedicated to investigating the physics governing the magnetism and dynamics in the lower solar atmosphere. The observatory is designed to operate in the stratosphere, at heights around 36 km (above 99% of Earth's atmosphere), to avoid image degradation due to turbulence in the Earth's lower

The Carina Nebula is an active star-forming region with several open clusters rich in massive OB stars, thus making it an optimal target for studying stellar properties such as rotation for large samples of these early-type stars. We studied a sample of early-type stars probable members of the eight open clusters in the Carina Complex. The

Context. While transmission spectroscopy has allowed us to detect many atomic and molecular species in exoplanet atmospheres, the improvement in resolution and signal-to-noise ratio (S/N) enabled us to become sensitive to planet-occulted line distortions (POLDs) in the spectrum that are induced by center-to-limb variations (CLV) and the Rossiter

The ability of any Machine Learning method to classify the spectra of galaxies depending on the properties of the stellar component rests on the information content of the data. The well-known degeneracies found in population synthesis models suggest this information might be so entangled as to challenge the most sophisticated Deep Learning