  The angular power spectrum is a natural tool to analyse the observed galaxy
number count fluctuations. In a standard analysis, the angular galaxy
distribution is sliced into concentric redshift bins and all correlations of
its harmonic coefficients between bin pairs are considered---a procedure
referred to as `tomography'. However, the unparalleled quality of data from
oncoming spectroscopic galaxy surveys for cosmology will render this method
computationally unfeasible, given the increasing number of bins. Here, we put
to test against synthetic data a novel method proposed in a previous study to
save computational time. According to this method, the whole galaxy redshift
distribution is subdivided into thick bins, neglecting the cross-bin
correlations among them; each of the thick bin is, however, further subdivided
into thinner bins, considering in this case all the cross-bin correlations. We
create a simulated data set that we then analyse in a Bayesian framework. We
confirm that the newly proposed method saves computational time and gives
results that surpass those of the standard approach.
