Giant exoplanets orbiting close to their host stars are unlikely to have formed in their present configurations 1. These `hot Jupiter' planets are instead thought to have migrated inward from beyond the ice line and several viable migration channels have been proposed, including eccentricity excitation through angular-momentum exchange with a third

Observing exoplanets through transmission spectroscopy supplies detailed information about their atmospheric composition, physics and chemistry. Before the James Webb Space Telescope (JWST), these observations were limited to a narrow wavelength range across the near-ultraviolet to near-infrared, alongside broadband photometry at longer wavelengths

Observations of nearby massive galaxies have revealed that they are older and richer in metals and magnesium than their low-mass counterparts. In particular, the overabundance of magnesium compared to iron, [Mg/Fe], is interpreted to reflect the short star formation history that the current massive galaxies underwent early in the Universe. We

The Hubble and James Webb Space Telescopes, with their outstanding depth and spatial resolution, are opening up the possibility of studying the edges of disk galaxies over a significant cosmic time interval. This allows us to study in detail the evolution of galaxy edges over the past 8 Gyr. The edge of galaxies, understood here as the farthest

Interstellar gas offers advantages when quantifying the structure of the resonances in disc galaxies. The gas is concentrated close to the disc mid-plane, making it easier to determine its in-plane velocity components, and it offers sharp emission lines for good velocity resolution. These properties are shared by atomic, molecular, and ionized gas

Context. Deep learning (DL) techniques are a promising approach among the set of methods used in the ever-challenging determination of stellar parameters in M dwarfs. In this context, transfer learning could play an important role in mitigating uncertainties in the results due to the synthetic gap (i.e. difference in feature distributions between