Catalogue of radial velocity predictions for Gaia DR3
Description
This catalogue accompanies our article (Naik & Widmark, 2022), in which we demonstrate the use of Bayesian neural networks for predicting the missing radial (line-of-sight) velocities of stars observed by the Gaia satellite.
The catalogue contains predictions for all Gaia stars in the magnitude range 6<G<14.5 which have distance estimates from StarHorse but do not have radial velocity measurements. This is around 17 million stars.
The catalogue contains 2 files, a larger file containing the source_ids and 250 posterior samples for each star, and a smaller file containing just the source_ids and the 5th/16th/50th/84th/95th percentile values.
External URI
Related publication DOI
Subjects
- Gaia hypothesis
- Astronomy
- Machine learning
- Stars
- Gaia, radial velocity, stars, astronomy, machine learning, Bayesian neural network
- Physical sciences::Astronomy::Astronomy observation
- Q Science::QB Astronomy
Divisions
- University of Nottingham, UK Campus::Faculty of Science::School of Physics and Astronomy
Deposit date
2022-06-10Data type
Numerical dataContributors
- Widmark, Axel
Funders
- Other
- Leverhulme Trust
Data collection method
Numerical predictions from Bayesian neural networkResource languages
- en