Optimising primordial non-Gaussianity measurements from galaxy surveys
  Galaxy clustering data from current and upcoming large scale structure
surveys can provide strong constraints on primordial non-Gaussianity through
the scale-dependent halo bias. To fully exploit the information from galaxy
surveys, optimal analysis methods need to be developed and applied to the data.
Since the halo bias is sensitive to local non-Gaussianity predominately at
large scales, the volume of a given survey is crucial. Consequently, for such
analyses we do not want to split into redshift bins, which would lead to
information loss due to edge effects, but instead analyse the full sample. We
present an optimal technique to directly constrain local non-Gaussianity
parametrised by $f_\mathrm{NL}^\mathrm{loc}$, from galaxy clustering by
applying redshift weights to the galaxies. We derive a set of weights to
optimally measure the amplitude of local non-Gaussianity,
$f_\mathrm{NL}^\mathrm{loc}$, discuss the redshift weighted power spectrum
estimators, outline the implementation procedure and test our weighting scheme
against Lognormal catalogs for two different surveys: the quasar sample of the
Extended Baryon Oscillation Spectroscopic Survey (eBOSS) and the emission line
galaxy sample of the Dark Energy Spectroscopic Instrument (DESI) survey. We
find an improvement of 30 percent for eBOSS and 6 percent for DESI compared to
the standard Feldman, Kaiser $\&$ Peacock weights, although these predictions
are sensitive to the bias model assumed.
