activity
20142025
most citedThe Gaia mission

7.1k citations · 9.5k across the 13 of their papers we have counts for

collaborators

10 papers

astro-ph.GA2024129 cited

Discovery of a dormant 33 solar-mass black hole in pre-release Gaia astrometry

Gaia Collaboration, P. Panuzzo, T. Mazeh +412

Gravitational waves from black-hole merging events have revealed a population of extra-galactic BHs residing in short-period binaries with masses that are higher than expected base…

astro-ph.GA2024

Uniting Gaia and APOGEE to unveil the cosmic chemistry of the Milky Way disc

Tristan Cantat-Gaudin, Morgan Fouesneau, Hans-Walter Rix +6

The spatial distribution of Galactic stars with different chemical abundances encodes information on the processes that drove the formation and evolution of the Milky Way. Survey s…

astro-ph.IM20232 cited

Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers

D. Huppenkothen, M. Ntampaka, M. Ho +19

Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of…

astro-ph.SR2023

Gaia Focused Product Release: Radial velocity time series of long-period variables

Gaia Collaboration, M. Trabucchi, N. Mowlavi +403

The third Gaia Data Release (DR3) provided photometric time series of more than 2 million long-period variable (LPV) candidates. Anticipating the publication of full radial-velocit…

astro-ph.GA2023

Dissecting the Gaia HR diagram II. The vertical structure of the star formation history across the Solar Cylinder

Alessandro Mazzi, Léo Girardi, Michele Trabucchi +11

Starting from the Gaia DR3 HR diagram, we derive the star formation history (SFH) as a function of distance from the Galactic Plane within a cylinder centred on the Sun with a 200~…

astro-ph.GA201638 cited

Inferring the three-dimensional distribution of dust in the Galaxy with a non-parametric method: Preparing for Gaia

S. Rezaei Kh., C. A. L. Bailer-Jones, R. J. Hanson +1

We present a non-parametric model for inferring the three-dimensional (3D) distribution of dust density in the Milky Way. Our approach uses the extinction measured towards stars at…