We present the first measurement of the Lyα forest baryon acoustic oscillations (BAO) shift parameter from cosmological simulations. In particular, we generate a suite of 1000 accurate effective field-level bias-based Lyα forest simulations of volume at z = 2, both in real and redshift space, calibrated upon two fixed-and-paired cosmological

We obtain a quantitative star formation history (SFH) of a shell-like structure ('shell') located in the northeastern part of the Small Magellanic Cloud (SMC). We use the Survey of the MAgellanic Stellar History to derive colour-magnitude diagrams (CMDs), reaching below the oldest main-sequence turnoff, from which we compute the SFHs with CMD

TOI-6255 b (GJ 4256) is an Earth-sized planet (1.079 ± 0.065 R ⊕) with an orbital period of only 5.7 hr. With the newly commissioned Keck Planet Finder and CARMENES spectrographs, we determine the planet's mass to be 1.44 ± 0.14 M ⊕. The planet is just outside the Roche limit, with P orb/P Roche = 1.13 ± 0.10. The strong tidal force likely deforms

We report the spectroscopic confirmation and fundamental properties of TOI$-$757 b, a mini$-$Neptune on a 17.5$-$d orbit transiting a bright star ($V\, =\, 9.7$ mag) discovered by the TESS mission. We acquired high$-$precision radial velocity measurements with the HARPS, ESPRESSO, and PFS spectrographs to confirm the planet detection and determine

Context. Blue supergiants (BSGs) are key objects for understanding the evolution of massive stars, which play a crucial role in the evolution of galaxies. However, discrepancies between theoretical predictions and empirical observations have opened up important questions yet to be answered. Studying statistically significant and unbiased samples of

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