The solar corona—the outermost layer of the Sun’s atmosphere—is extremely hot and very low in density. One of the main challenges in solar physics is understanding why the corona reaches temperatures of over a million degrees. This heating is believed to be closely related to the Sun’s magnetic field. However, quantifying the coronal magnetic field is difficult because the light emitted by the corona is extremely faint, and its polarization signals, which encode the information on the magnetic field, are subtle. Thanks to recent advances in technology, telescopes like the Daniel K. Inouye

The TESS (Transiting Exoplanet Survey Satellite) mission has discovered many exoplanet candidates that need to be confirmed and characterized from the ground. One of them orbits Ross 176, a K-type dwarf star, where we have identified a promising hot “water-world” candidate. Using spectroscopic observations with the CARMENES instrument, we confirmed the planetary nature of the signal detected by TESS and estimated the planet’s mass. To improve the analysis, we applied an advanced statistical method called Gaussian Process, which allowed us to separate the star’s own variability (quite strong

Light bridges are elongated and bright structures protruding into the umbra of sunspots. The presence of light bridges has a significant role in the evolution of sunspots and the heating of their overlying atmosphere. Therefore, investigating these structures is crucial to understanding fundamental aspects of sunspots. By applying a novel code based on deep-learning algorithms called SICON to spectropolarimetric observations acquired with the Hinode satellite, we computed atmospheric parameters that allowed us to infer the variation of the physical properties of light bridges on a geometric