estimates_gdr3_rv (gaiadr3_contrib.estimates_gdr3_rv)
The table has 184905568 rows, 20 columns.
Description
Radial velocity predictions for all Gaia DR3 stars without radial velocity measurements and with G-band magnitude brighter than 17.5. The predictions are generated by a Bayesian neural network model trained on the DR3 RVS sample. The model generates full posterior distributions, which are summarised in this catalogue as 4-component Gaussian mixtures.
Attribution
Aneesh Naik is funded by an Early Career Fellowship from the Leverhulme Trust.
Axel Widmark receives funding from the Carlsberg Foundation via a Semper Ardens grant (CF15-0384).
Training the model and generating the catalogue used the Cirrus UK National Tier-2 HPC Service at EPCC (http://www.cirrus.ac.uk) funded by the University of Edinburgh and EPSRC (EP/P020267/1).
Columns
| Name | Type | UCD | Unit | Description |
|---|---|---|---|---|
| source_id | long | meta.id |
Unique source identifier (unique within a particular Data Release) |
|
| sample_mean | float |
stat.mean spect.dopplerVeloc |
km.s**-1 |
Mean of radial velocity posterior |
| sample_std | float |
stat.stdev spect.dopplerVeloc |
km.s**-1 |
Standard deviation of radial velocity posterior |
| q050 | float |
stat.value spect.dopplerVeloc |
km.s**-1 |
5th percentile of radial velocity posterior |
| q159 | float |
stat.value spect.dopplerVeloc |
km.s**-1 |
15.9th percentile velocities for each star |
| q500 | float |
stat.value spect.dopplerVeloc |
km.s**-1 |
50th percentile velocities for each star |
| q841 | float |
stat.value spect.dopplerVeloc |
km.s**-1 |
84.1th percentile velocities for each star |
| q950 | float |
stat.value spect.dopplerVeloc |
km.s**-1 |
95.5th percentile velocities for each star |
| w_0 | float | stat.weight |
Weight of Gaussian component 0. (w0+w1+w2+w3) = 1.0 |
|
| w_1 | float | stat.weight |
Weight of Gaussian component 1. (w0+w1+w2+w3) = 1.0 |
|
| w_2 | float | stat.weight |
Weight of Gaussian component 2. (w0+w1+w2+w3) = 1.0 |
|
| w_3 | float | stat.weight |
Weight of Gaussian component 2. (w0+w1+w2+w3) = 1.0 |
|
| mu_0 | float |
stat.mean spect.dopplerVeloc |
km.s**-1 |
Mean of Gaussian component 0. |
| mu_1 | float |
stat.mean spect.dopplerVeloc |
km.s**-1 |
Mean of Gaussian component 1. |
| mu_2 | float |
stat.mean spect.dopplerVeloc |
km.s**-1 |
Mean of Gaussian component 2. |
| mu_3 | float |
stat.mean spect.dopplerVeloc |
km**2.s**-2 |
Mean of Gaussian component 3. |
| var_0 | float | stat.variance | km**2.s**-2 |
Variance of Gaussian component 0. |
| var_1 | float | stat.variance | km**2.s**-2 |
Variance of Gaussian component 1. |
| var_2 | float | stat.variance | km**2.s**-2 |
Variance of Gaussian component 2. |
| var_3 | float | stat.variance | km**2.s**-2 |
Variance of Gaussian component 3. |