TOI-1227 b is an 11 Myr old validated transiting planet in the middle of its contraction phase, with a current radius of 0.85 R j. It orbits a low-mass pre-main sequence star (0.170 M ⊙, 0.56 R ⊙) every 27.4 days. The magnetic activity of its young host star induces radial velocity jitter and prevents good measurements of the planetary mass. We

Blue supergiants are the brightest stars in their host galaxies, and yet their evolutionary status has been a long-standing problem in stellar astrophysics. In this pioneering work, we present a large sample of 59 early B-type supergiants in the Large Magellanic Cloud with newly derived stellar parameters and identify the signatures of stars born

To obtain an accurate cosmological inference from upcoming weak lensing surveys such as the one conducted by Euclid, the shear measurement requires calibration using galaxy image simulations. As it typically requires millions of simulated galaxy images and consequently a substantial computational effort, seeking methods to speed the calibration up

Aims: We validate a semi-analytical model for the covariance of the real-space two-point correlation function of galaxy clusters. Methods: Using 1000 PINOCCHIO light cones mimicking the expected Euclid sample of galaxy clusters, we calibrated a simple model to accurately describe the clustering covariance. Then, we used this model to quantify the

Context. The cosmological surveys that are planned for the current decade will provide us with unparalleled observations of the distribution of galaxies on cosmic scales, by means of which we can probe the underlying large-scale structure (LSS) of the Universe. This will allow us to test the concordance cosmological model and its extensions

Modern cosmological hydrodynamical galaxy simulations provide tens of thousands of reasonably realistic synthetic galaxies across cosmic time. However, quantitatively assessing the level of realism of simulated universes in comparison to the real one is difficult. In this paper of the Extracting Reality from Galaxy Observables with Machine Learning