We present multiwavelength analysis of ZTF23abelseb (AT 2023sva), an optically discovered fast-fading ( $\Delta m_r = 2.2$ mag in $\Delta t = 0.74$ d), luminous ( $M_r \sim -30.0$ mag), and red ( $g-r = 0.50$ mag) transient at $z = 2.28$ with accompanying luminous radio emission. AT 2023sva does not possess a $\gamma$-ray burst (GRB) counterpart to

Cosmic shear, galaxy clustering, and the abundance of massive halos each probe the large-scale structure of the Universe in complementary ways. We present cosmological constraints from the joint analysis of the three probes, building on the latest analyses of the lensing-informed abundance of clusters identified by the South Pole Telescope (SPT)

Context. Gaia Bp/Rp spectrophotometry for over two hundred million stars has been publicly released as part of Gaia Data Release 3 (DR3). These data have great potential for mapping metallicity across the Milky Way. Several recent studies have analyzed this data set to derive atmospheric parameters and identify new metal-poor stars. In addition

Context. UDG 32 is an ultra-diffuse galaxy (UDG) candidate in the Hydra I cluster that was discovered in the extended network of stellar filaments of the jellyfish galaxy NGC 3314A. This jellyfish galaxy is affected by ram pressure stripping and it is hypothesised that UDG 32 may have formed from this stripped material. Aims. The aim of this paper

The weak gravitational lensing magnification of Type Ia supernovae (SNe Ia) is sensitive to the matter power spectrum on scales $k\gt 1 h$ Mpc $^{-1}$, making it unwise to interpret SNe Ia lensing in terms of power on linear scales. We compute the probability density function of SNe Ia magnification as a function of standard cosmological parameters

Context. Currently, the number of known planets around intermediate-mass stars (1.5 M ⊙ < M ⋆ < 3.5 M ⊙) is rather low. As a consequence, models of planet formation derive their strongest observational evidence from the chemical signature of mostly low-mass (FGK) main-sequence (MS) stars with planets. Aims. We aim to test whether the well-known