  Multiple oscillation frequencies separated by close to or less than the
formal frequency resolution of a data set are a serious problem in the
frequency analysis of time series data. We present a new and fully automated
Bayesian approach that searches for close frequencies in time series data and
assesses their significance by comparison to no signal and a mono-periodic
signal. We extensively test the approach with synthetic data sets and apply it
to the 156 days-long high-precision BRITE photometry of the SPB star HD 201433,
for which we find a sequence of nine statistically significant rotationally
split dipole modes.
