One of the main goals of the NASA Transiting Exoplanet Survey Satellite (TESS) mission is the discovery of Earth-like planets around nearby M-dwarf stars. We present the discovery and validation of three new short-period Earth-sized planets orbiting nearby M dwarfs: TOI-5720 b, TOI-6008 b, and TOI-6086 b. We combined TESS data, ground-based

We adopt a Bayesian X-ray spectral approach to investigate the accretion properties of unobscured ($20\lt \log (N_{\rm H}/{\rm cm}^{-2}\lt 22$) and obscured ($22\lt \log (N_{\rm H}/{\rm cm}^{-2}\lt 24$) active galactic nuclei (AGNs) to shed light on the orientation versus evolution scenarios for the origin of the obscuring material. For a sample of

Highly r-process-enhanced (RPE) stars are rare and usually metal poor ([Fe/H] < ‑1.0), and they mainly populate the Milky Way halo and dwarf galaxies. This study presents the discovery of a relatively bright (V = 12.72), highly RPE (r-II) star ([Eu/Fe] = +1.32, [Ba/Eu] = ‑0.95), LAMOST J020623.21+494127.9. This star was selected from the Large Sky

Red Geysers are galaxies with low-star formation rates and galactic scale ionized outflows likely driven by low-luminosity active galactic nuclei (AGN). We investigated the impact of AGN winds on the oxygen abundance using integral field spectroscopic data from Mapping Nearby Galaxies for Red Geysers, control galaxies (quiescent galaxies without

OPTICAM is a triple-band optical system developed for the 2.1 m telescope of the National Astronomical Observatory in the Sierra de San Pedro Mártir (OAN-SPM). Partial engineering tests were conducted in 2019, with the complete system experiencing its first light in March 2022. The system incorporates two beam splitters, enabling simultaneous

We investigate the accuracy of the perturbative galaxy bias expansion in view of the forthcoming analysis of the Euclid spectroscopic galaxy samples. We compare the performance of a Eulerian galaxy bias expansion using state-of-the-art prescriptions from the effective field theory of large-scale structure (EFTofLSS) with a hybrid approach based on