2020-11-13T13:09:26Z
Geometric
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/form
milky-way-galaxy
stellar-distance
surveys
stars
Bailer-Jones, C.A.L.; Rybizki, J.;
Fouesneau, M.; Demleitner, M.; Andrae, R.
Gaia
2021AJ....161..147B
Research
Catalog
Optical
3000
3000
For each source we compute two posterior probability distributions over
distance: a geometric one and a photogeometric one. “Geometric” means
only parallax and the parallax uncertainty were used. “Photogeometric”
means the G magnitude and the BP-RP colour were used as well. For each
of these posterior distributions we estimate and provide three quantiles:
0.158655 (“Lo“), 0.5 (“Med”), and 0.841345 (“Hi”).
“Med” is the median of the distribution, and should be taken as the
distance estimate itself. “Lo” and “Hi” define the lower and upper ends
of the equal-tailed 68% (actually 68.269%) confidence interval on this
estimate. If the posterior were Gaussian, then (r_hi_geo-r_lo_geo)/2
would be the 1-σ Gaussian uncertainty of the geometric distance (and
similarly for the photogeometric distance). However, we stress that
these confidence bounds are asymmetric, sometimes significantly so.
The distance estimates are predicated on the assumption that the source
is a single star in our Galaxy. Estimates are provided whereever
possible for sources that have the required input data, independent of
any other knowledge on the nature of that source (e.g. being a binary
star or quasar).
G_lim for that HEALpixel
:B:
The first (left-most) digit refers to the geometric posterior, the
second to the photogeometric posterior.
It indicates whether we have a low p-value (<1e-3) in the Hartigan Dip test
(null hypothesis that posterior is unimodal, so small p suggests
evidence against this). Two-digit integer. Each digit can be:
:0:
not set, so assume unimodal hypothesis okay (or if test is not done
or gives no answer)
:1:
set, so possibly multimodal.
For instance, 10 means geo possibly multimodal, photgeo probably unimodal
Moreover, the computed confidence interval often spans any multimodality, so
generally speaking sources do not need to be excluded just because of
evidence of multimodality from this test. This flag refers to the
posterior as sampled by the MCMC rather than the true posterior.
:C:
QG models used to compute the photogeometric posterior. Two digit integer.
Each digit can be:
:0:
NULL
:1:
one-component Gaussian model
:2:
two-component Gaussian model
:3:
smoothing spline
The first (left-most) digit refers to the lower (bluer) model, the second to
the upper (redder) model.
E.g. 13 means the lower one was a one-component Gaussian and the upper
one was was spline.
There is also a special setting of this flag:
:99:
data (G or BP-RP) were missing (so no photogeo distance could be computed)
.. _accompanying paper: http://www.mpia.de/homes/calj/gedr3_distances.html
]]>
source_id
0/0-11
make_view
Download guard for gedr3dist dump
res/downloadguard.html
```
from gavo import svcs
if inputTable.getParam("input")=="yes":
raise svcs.WebRedirect(
"http://vo.ari.uni-heidelberg.de/gedr3dist/gedr3dist.dump.gz")
else:
raise base.ValidationError("This must be 'yes' (without any"
" quotes)", "input")
```

Gaia (e)DR3 lite distances subsetThis table joins the DR3 "lite" table
(consisting only of the columns necessary for the most basic
science) with the estimated geometric and photogeometric distances.
Note that this is an inner join, i.e., DR3 objects without
distance estimates will not show up here.
Note: Due to current limitations of the postgres query planner,
this table cannot usefully be used in positional joins
("crossmatches"). See the `Tricking the query planner`_ example.
.. _tricking the query planner: http://dc.g-vo.org/tap/examples#Trickingthequeryplanner
CREATE VIEW \qName AS (
SELECT \colNames FROM (
\schema.main
JOIN gaia.dr3lite
USING (source_id)))
Gaia DR3 Lite Distances Subset Cone Search
This service returns the most important
Gaia DR3 gaia_source columns together with robust geometric and
photogeometric distances for the ~1.47 billion objects in Bailer-Jones
et al's distance catalogue.
DR3 lite+dist
testQuery.ra: 303.28511
testQuery.dec: 40.92948
testQuery.sr: 0.001
data
cone/scs.xml
```
row = self.getFirstVOTableRow()
self.assertEqual(row["source_id"], "2062599774097558784")
self.assertAlmostEqual(row["pmra"], 0.08117900043725967)
self.assertAlmostEqual(row["r_lo_photogeo"], 2672.26171875)
```