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Catalogue of radial velocity predictions for Gaia DR3

Publication Date
June 10, 2022
Creators
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
External URI
DOI
Associate publ. DOI
Subjects (LCSH)

Gaia hypothesis

Astronomy

Machine learning

Stars

Subjects

Gaia, radial velocity...

Subjects (JACS)

Physical sciences::As...

Subjects (LC)

Q Science::QB Astrono...

Divisions
University of Nottingham, UK Campus::Faculty of Science::School of Physics and Astronomy
Data type
Numerical data
Funders
Funders
Leverhulme Trust
Data collection method
Numerical predictions from Bayesian neural network
Resource languages
English
Publisher
University of Nottingham
Date Issued
June 10, 2022
Except where otherwise noted, this item's license is described as
File(s)
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Name

EDR3MissingRVCatalogue.hdf5

Description
Larger prediction catalogue: 250 posterior samples for each star, along with Gaia source_id
Size

15.48 GB

Format

Unknown

Checksum (MD5)

4b69cedabca96deda83238869006bdf5

Thumbnail Image
Name

EDR3MissingRVCatalogueLight.hdf5

Description
Smaller prediction catalogue: 5th/16th/50th/84th/95th posterior percentiles for each star, along with Gaia source_id
Size

570.65 MB

Format

Unknown

Checksum (MD5)

e8ee83276c09f629224abaf68ce66e46