Fluid and ultralight bosonic dark matter can interact through gravity to form stable fermion-boson stars, which are static and regular mixed solutions of the Einstein-Euler-(complex, massive)-Klein-Gordon system. In this work we study the dynamical formation via gravitational cooling of a spherical mixed white-dwarf-boson star, whose properties

Context. Submillimeter galaxies (SMGs) have been proposed as the progenitors of massive ellipticals in the local Universe. Mapping the neutral gas distribution and investigating the gas accretion toward the SMGs at high redshift can provide information on the way SMG environments can evolve into clusters at z = 0. Aims: In this work, we study the

We present the third data release (DR3) of the X-shooter Spectral Library (XSL). This moderate-to-high resolution, near-ultraviolet-to-near-infrared (350-2480 nm, R ∼ 10 000) spectral library is composed of 830 stellar spectra of 683 stars. DR3 improves upon the previous data release by providing the combined de-reddened spectra of the three X

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release

We use Gaia photometry and astrometry to estimate the vertical spatial structure of the Milky Way at the Solar radius, formally accounting for sample incompleteness (the selection function) and parallax measurement uncertainty. Our results show impressive precision demonstrating the power of the Gaia data. However, systematic errors dominate the

We introduce a method to infer the vertical distribution of stars in the Milky Way using a Poisson likelihood function, with a view to applying our method to the Gaia catalogue. We show how to account for the sample selection function and for parallax measurement uncertainties. Our method is validated against a simulated sample drawn from a model