  Context. Distinguishing between a signal induced by either stellar activity
or a planet is currently the main challenge in radial velocity searches for
low-mass exoplanets. Even when the presence of a transiting planet and hence
its period are known, stellar activity can be the main barrier to measuring the
correct amplitude of the radial velocity signal. Several tools are being used
to help understand which signals come from stellar activity in the data. Aims.
We aim to present a new tool that can be used for the purpose of identifying
periodicities caused by stellar activity, and show how it can be used to track
the signal-to-noise ratio (SNR) of the detection over time. The tool is based
on the principle that stellar activity signals are variable and incoherent.
Methods. We calculate the Bayesian general Lomb-Scargle periodogram for subsets
of data and by adding one extra data point we track what happens to the
presence and significance of periodicities in the data. Publicly available
datasets from HARPS and HARPS-N were used for this purpose. Additionally, we
analysed a synthetic dataset that we created with SOAP2.0 to simulate pure
stellar activity and a mixture of stellar activity and a planet. Results. We
find that this tool can easily be used to identify unstable and incoherent
signals, such as those introduced by stellar activity. The SNR of the detection
grows approximately as the square root of the number of data points, in the
case of a stable signal. This can then be used to make decisions on whether it
is useful to keep observing a specific object. The tool is relatively fast and
easy to use, and thus lends itself perfectly to a quick analysis of the data.
