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In an earlier work, we demonstrated the effectiveness of Bayesian neuralnetworks in estimating the missing line-of-sight velocities of Gaia stars, andpublished an accompanying catalogue of blind predictions for the line-of-sightvelocities of stars in Gaia DR3. These were not merely point predictions, butprobability distributions reflecting our state of knowledge about each star.Here, we verify that these predictions were highly accurate: the DR3measurements were statistically consistent with our prediction distributions,with an approximate error rate of 1.5%. We use this same technique to produce apublicly available catalogue of predictive probability distributions for the185 million stars up to a G-band magnitude of 17.5 still missing line-of-sightvelocities in Gaia DR3. Validation tests demonstrate that the predictions arereliable for stars within approximately 7 kpc from the Sun and with distanceprecisions better than around 20%. For such stars, the typical predictionuncertainty is 25-30 km/s. We invite the community to use these radialvelocities in analyses of stellar kinematics and dynamics, and give an exampleof such an application.

The missing radial velocities of Gaia: a catalogue of Bayesian estimates for DR3