2020-12-21T09:17:31Z
20
A classifier for spurious astrometric solutions in Gaia EDR3
This table contains estimates of the "fidelity" of Gaia eDR3
astrometric solutions, a measure of the likelihood the eDR3
solution is physical rather than spurious obtained using a neural
network trained on a small, hand-selected sample.
stellar-distance
trigonometric-parallax
measurement-error-model
Rybizki, J.; Green, G.M.; Rix, H.-W.; El-Badry, K.;
Demleitner, M.; Zari, E.; Udalski, A.; Smart, R.L; Gould, A.
Neural Network
Gaia
2022MNRAS.510.2597R
Research
Catalog
Corrected distances, norm_dg and theta_arcsec_worst_source
for data coming form level 6 healpix healpix 3; these were wrong
due to mixed up input columns. The fidelity scores were unaffected.
Optical
The full data set is also available as a postgres dump (basically,
tab-separated values with ``\N`` as NULL). This dump is sorted by
source_ids, so you can bisect in it.
To obtain the dump (which we don't link here to keep crawlers from
needlessly pulling 48 GB of data), run your preferred variant of::
curl -O http://vo.ari.uni-heidelberg.de/gedr3spur/dump.txt.gz
For the metadata, inspect the column metadata below in database order.
/tap/sync
row = self.getFirstVOTableRow()
self.assertAlmostEqual(row["fidelity_v2"], 0.99951171875)
self.assertEqual(row["fidelity_v1"], 1)
self.assertEqual(row["norm_dg"], None)