Related papers: Transferring spectroscopic stellar labels to 217 m…
The third Gaia data release (DR3) contains $\sim$170\,000 astrometric orbit solutions of two-body systems located within $\sim$500 pc of the Sun. Determining component masses in these systems, in particular of stars hosting exoplanets,…
Gaia Data Release 3 will contain more than a billion sources with positions, parallaxes, and proper motions. In addition, for hundreds of millions of stars, it will include low-resolution blue photometer (BP) and red photometer (RP)…
Data-driven models for stellar spectra which depend on stellar labels suffer from label systematics which decrease model performance: the "stellar labels gap". To close the stellar labels gap, we present a stellar label independent model…
Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar…
We combine Gaia Data Release 3 and artificial intelligence to enhance the current statistics of substellar companions, particularly within regions of the orbital period vs. mass parameter space that remain poorly constrained by the radial…
We present a catalogue of 362 million stellar parameters, distances, and extinctions derived from Gaia's early third data release (EDR3) cross-matched with the photometric catalogues of Pan-STARRS1, SkyMapper, 2MASS, and AllWISE. The higher…
We estimate ([M/H], [$\alpha$/M]) for 48 million giants and dwarfs in low-dust extinction regions from the Gaia DR3 XP spectra by using tree-based machine-learning models trained on APOGEE DR17 and metal-poor star sample \revise{from} Li et…
Despite the advances provided by large-scale photometric surveys, stellar features - such as metallicity - generally remain limited to spectroscopic observations often of bright, nearby low-extinction stars. To rectify this, we present a…
Accurate determinations of stellar parameters and distances for large complete samples of stars are keys for conducting detailed studies of the formation and evolution of our Galaxy. Here we present stellar atmospheric parameters ($T_{\rm…
The recent release of 220+ million BP/RP spectra in $\textit{Gaia}$ DR3 presents an opportunity to apply deep learning models to an unprecedented number of stellar spectra, at extremely low-resolution. The BP/RP dataset is so massive that…
Gaia DR3 provides radial velocities for 33 million stars and spectroscopically derived atmospheric parameters for more than five million targets. When combined with the astrometric data, these allow us to derive orbital and stellar…
Gaia will obtain astrometry and spectrophotometry for essentially all sources in the sky down to a broad band magnitude limit of G=20, an expected yield of 10^9 stars. Its main scientific objective is to reveal the formation and evolution…
Very metal-poor stars ($\rm[Fe/H] < -2$) in the Milky Way are fossil records of early chemical evolution and the assembly and structure of the Galaxy. However, they are rare and hard to find. Gaia DR3 has provided over 200 million…
Context. The study of the oldest and most metal-poor stars in our Galaxy promotes our understanding of the Galactic chemical evolution and the beginning of Galaxy and star formation. However, they are notoriously difficult to find, with…
Context. The third Gaia Data Release, which includes BP/RP spectra for 219 million sources, has opened a new window in the exploration of the chemical history and evolution of the Milky Way. The wealth of information encapsulated in these…
We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that…
The fundamental parameters of a low-mass star can potentially be determined from its photometry and astrometry. This is complicated by the fact that 10-20 percent of low-mass stars are predicted to be equal-mass binaries. These unresolved…
Machine learning can play a powerful role in inferring missing line-of-sight velocities from astrometry in surveys such as Gaia. In this paper, we apply a neural network to Gaia Early Data Release 3 (EDR3) and obtain line-of-sight…
We develop a data-driven model to map stellar parameters (effective temperature, surface gravity and metallicity) accurately and precisely to broad-band stellar photometry. This model must, and does, simultaneously constrain the…
The precision of cluster parameter determination has significantly improved with the availability of homogeneous photometric Gaia data, however, challenges such as age-metallicity degeneracy and lack of spectroscopic observations remain. In…