Optimal observables in galaxy surveys

TL;DR

This paper derives optimal local transformations for galaxy survey data to maximize information extraction about large-scale structures, using perturbation theory and simulation tests.

Contribution

It introduces a family of power transformations, including the logarithmic case, for optimal data analysis in galaxy surveys, accounting for Poisson noise.

Findings

Logarithmic transformation is optimal near spectral index -1.

Transformations improve information capture in large-scale structure analysis.

Validated methods with Millennium simulation data.

Abstract

The sufficient statistics of the one-point probability density function of the dark matter density field is worked out using cosmological perturbation theory and tested to the Millennium simulation density field. The logarithmic transformation is recovered for spectral index close to $-1$ as a special case of the family of power transformations. We then discuss how these transforms should be modified in the case of noisy tracers of the field and focus on the case of Poisson sampling. This gives us optimal local transformations to apply to galaxy survey data prior the extraction of the spectrum in order to capture most efficiently the information encoded in large scale structures.