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dc.contributor.authorNaik, Aneesh
dc.contributor.otherWidmark, Axel
dc.date.accessioned2022-06-10T08:53:12Z
dc.date.available2022-06-10T08:53:12Z
dc.date.issued2022-06-10
dc.identifier.urihttps://rdmc.nottingham.ac.uk/handle/internal/9534
dc.description.abstractThis 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.en_UK
dc.language.isoenen_UK
dc.publisherUniversity of Nottinghamen_UK
dc.rightsCC-BY*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subject.lcshGaia hypothesisen_UK
dc.subject.lcshAstronomyen_UK
dc.subject.lcshMachine learningen_UK
dc.subject.lcshStarsen_UK
dc.titleCatalogue of radial velocity predictions for Gaia DR3en_UK
dc.identifier.doihttp://doi.org/10.17639/nott.7216
dc.subject.freeGaia, radial velocity, stars, astronomy, machine learning, Bayesian neural networken_UK
dc.subject.jacsPhysical sciences::Astronomy::Astronomy observationen_UK
dc.subject.lcQ Science::QB Astronomyen_UK
uon.divisionUniversity of Nottingham, UK Campus::Faculty of Science::School of Physics and Astronomyen_UK
uon.funder.controlledOtheren_UK
uon.datatypeNumerical dataen_UK
uon.funder.freeLeverhulme Trusten_UK
uon.collectionmethodNumerical predictions from Bayesian neural networken_UK
dc.relation.doi10.48550/arXiv.2206.04102en_UK


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