  A new apodized Keplerian (AK) model is proposed for the analysis of precision
radial velocity (RV) data to model both planetary and stellar activity (SA)
induced RV signals. A symmetrical Gaussian apodization function with unknown
width and center can distinguish planetary signals from SA signals on the basis
of the span of the apodization window. The general model for $m$ apodized
Keplerian signals includes a linear regression term between RV and the stellar
activity diagnostic $\log(R'hk)$, as well as an extra Gaussian noise term with
unknown standard deviation. The model parameters are explored using a Bayesian
fusion MCMC code. A differential version of the Generalized Lomb-Scargle
periodogram that employs a control diagnostic provides an additional way of
distinguishing SA signals and helps guide the choice of new periods. Results
are reported for a recent international RV blind challenge which included
multiple state of the art simulated data sets supported by a variety of stellar
activity diagnostics. In the current implementation, the AK method achieved a
reduction in SA noise by a factor of approximately 6. Final parameter estimates
for the planetary candidates are derived from fits that include AK signals to
model the SA components and simple Keplerians to model the planetary
candidates. Preliminary results are also reported for AK models augmented by a
moving average component that allows for correlations in the residuals.
