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

Ultracool dwarf stars are abundant, long-lived and uniquely suited to enable the atmospheric study of transiting terrestrial companions with the JWST. Among them, the most prominent is the M8.5V star TRAPPIST-1 and its seven planets. While JWST Cycle 1 observations have started to yield preliminary insights into the planets, they have also revealed

Gas giants transiting bright nearby stars provide crucial insights into planetary system formation and evolution mechanisms. Most of these planets show certain average characteristics, serving as benchmarks for our understanding of planetary systems. However, outliers like the planet we present in this study, WASP-193 b, offer unique opportunities

Context. Since the discovery of the first exoplanet almost three decades ago, the number of known exoplanets has increased dramatically. By beginning of the 2000s it was clear that dedicated facilities to advance our studies in this field were needed. The CHaracterising ExOPlanet Satellite (CHEOPS) is a space telescope specifically designed to

The Lorentz Invariance Violation (LIV), a proposed consequence of certain quantum gravity (QG) scenarios, could instigate an energy-dependent group velocity for ultra-relativistic particles. This energy dependence, although suppressed by the massive QG energy scale E_QG, expected to be on the level of the Planck energy 1.22 × 10 19 GeV, is

Monochromatic gamma-ray signals constitute a potential smoking gun signature for annihilating or decaying dark matter particles that could relatively easily be distinguished from astrophysical or instrumental backgrounds. We provide an updated assessment of the sensitivity of the Cherenkov Telescope Array (CTA) to such signals, based on