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All-sky searches for continuous gravitational waves are generally modeldependent and computationally costly to run. By contrast, SOAP is amodel-agnostic search that rapidly returns candidate signal tracks in thetime-frequency plane. In this work we extend the SOAP search to return broadBayesian posteriors on the astrophysical parameters of a specific signal model.These constraints drastically reduce the volume of parameter space that anyfollow-up search needs to explore, so increasing the speed at which candidatescan be identified and confirmed. Our method uses a machine learning technique,specifically a conditional variational auto-encoder, and delivers a rapidestimation of the posterior distribution of the four Doppler parameters of acontinuous wave signal. It does so without requiring a clear definition of alikelihood function, or being shown any true Bayesian posteriors in training.We demonstrate how the Doppler parameter space volume can be reduced by afactor of $\mathcal{O}(10^{-7})$ for signals of SNR 100.

Rapid parameter estimation for an all-sky continuous gravitational wave search using conditional varitational auto-encoders