Geometric and photogeometric distances to 1.47 billion stars in Gaia
Early Data Release 3 (eDR3)gedr3dist.mainivo://org.gavo.dc/gedr3dist/q/mainThe GAVO DC teamBailer-Jones, C.A.L.Rybizki, J.Fouesneau, M.Demleitner, M.Andrae, R.2022-08-26T08:08:44Z2022-08-29GAVO Data Center TeamMÃ¶nchhofstrasse 12-14, D-69120 Heidelberggavo@ari.uni-heidelberg.de++49 6221 54 1837milky-way-galaxystellar-distancesurveysstars
We estimate the distance from the Sun to sources in Gaia eDR3 that have
parallaxes. We provide two types of distance estimate, together with
their corresponding asymmetric uncertainties, using Bayesian posterior
density functions that we sample for each source. Our prior is based
on a detailed model of the 3D spatial, colour, and magnitude
distribution of stars in our Galaxy that includes a 3D map of
interstellar extinction.
The first type of distance estimate is purely geometric, in that it only
makes use of the Gaia parallax and parallax uncertainty. This uses a
direction-dependent distance prior derived from our Galaxy model. The
second type of distance estimate is photogeometric: in addition to
parallax it also uses the source's G-band magnitude and BP-RP
colour. This type of estimate uses the geometric prior together with a
direction-dependent and colour-dependent prior on the absolute magnitude
of the star.
Our distance estimate and uncertainties are quantiles, so are invariant
under logarithmic transformations. This means that our median estimate
of the distance can be used to give the median estimate of the distance
modulus, and likewise for the uncertainties.
For applications that cannot be satisfied through TAP, you can download
a `full table dump`_.
.. _full table dump: /gedr3dist/q/download/formhttp://dc.zah.uni-heidelberg.de/taphttps://dc.zah.uni-heidelberg.de/tapGaia0/0-11Opticalgedr3distGeometric and photogeometric distances to 1.47 billion stars in Gaia
Early Data Release 3 (eDR3)
We estimate the distance from the Sun to sources in Gaia eDR3 that have
parallaxes. We provide two types of distance estimate, together with
their corresponding asymmetric uncertainties, using Bayesian posterior
density functions that we sample for each source. Our prior is based
on a detailed model of the 3D spatial, colour, and magnitude
distribution of stars in our Galaxy that includes a 3D map of
interstellar extinction.
The first type of distance estimate is purely geometric, in that it only
makes use of the Gaia parallax and parallax uncertainty. This uses a
direction-dependent distance prior derived from our Galaxy model. The
second type of distance estimate is photogeometric: in addition to
parallax it also uses the source's G-band magnitude and BP-RP
colour. This type of estimate uses the geometric prior together with a
direction-dependent and colour-dependent prior on the absolute magnitude
of the star.
Our distance estimate and uncertainties are quantiles, so are invariant
under logarithmic transformations. This means that our median estimate
of the distance can be used to give the median estimate of the distance
modulus, and likewise for the uncertainties.
For applications that cannot be satisfied through TAP, you can download
a `full table dump`_.
.. _full table dump: /gedr3dist/q/download/formgedr3dist.main
We estimate the distance from the Sun to sources in Gaia eDR3 that have
parallaxes. We provide two types of distance estimate, together with
their corresponding asymmetric uncertainties, using Bayesian posterior
density functions that we sample for each source. Our prior is based
on a detailed model of the 3D spatial, colour, and magnitude
distribution of stars in our Galaxy that includes a 3D map of
interstellar extinction.
The first type of distance estimate is purely geometric, in that it only
makes use of the Gaia parallax and parallax uncertainty. This uses a
direction-dependent distance prior derived from our Galaxy model. The
second type of distance estimate is photogeometric: in addition to
parallax it also uses the source's G-band magnitude and BP-RP
colour. This type of estimate uses the geometric prior together with a
direction-dependent and colour-dependent prior on the absolute magnitude
of the star.
Our distance estimate and uncertainties are quantiles, so are invariant
under logarithmic transformations. This means that our median estimate
of the distance can be used to give the median estimate of the distance
modulus, and likewise for the uncertainties.
For applications that cannot be satisfied through TAP, you can download
a `full table dump`_.
.. _full table dump: /gedr3dist/q/download/form1470000000source_idGaia DR3 unique source identifier. Note that this *cannot* be matched against the DR1 or DR2 source_ids.meta.id;meta.mainlongindexedprimaryr_med_geoThe median of the geometric distance posterior. The geometric distance estimate.pcpos.distancefloatindexednullabler_lo_geoThe 16th percentile of the geometric distance posterior. The lower 1-sigma-like bound on the confidence interval.pcpos.distance;stat.minfloatnullabler_hi_geoThe 84th percentile of the geometric distance posterior. The upper 1-sigma-like bound on the confidence interval.pcpos.distance;stat.maxfloatnullabler_med_photogeoThe median of the photogeometric distance posterior. The photogeometric distance estimate.pcpos.distancefloatindexednullabler_lo_photogeoThe 16th percentile of the photogeometric distance posterior. The lower 1-sigma-like bound on the confidence interval.pcpos.distance;stat.minfloatnullabler_hi_photogeoThe 84th percentile of the photogeometric distance posterior. The upper 1-sigma-like bound on the confidence interval.pcpos.distance;stat.maxfloatnullableflagAdditional information on the solution. Do not use for filtering (see table note in the reference URL).meta.codecharnullable