Compressing combined probes: redshift weights for joint lensing and clustering analyses

TL;DR

This paper introduces a new data compression method for joint galaxy clustering and weak lensing analyses, optimizing information extraction and reducing computational costs for large datasets in upcoming dark energy surveys.

Contribution

It develops an optimal redshift weighting estimator that compresses combined probe data without information loss, enhancing efficiency in cosmological parameter estimation.

Findings

Redshift weights improve analysis sensitivity.

Method reduces computational complexity.

No information loss in data compression.

Abstract

Combining different observational probes, such as galaxy clustering and weak lensing, is a promising technique for unveiling the physics of the Universe with upcoming dark energy experiments. Whilst this strategy significantly improves parameter constraints, decreasing the degeneracies of individual analyses and controlling the systematics, processing data from tens of millions of galaxies is not a trivial task. In this work we derive and test a new estimator for joint clustering and lensing data analysis, maximising the scientific return and decreasing the computational cost. Our estimator compresses the data by up-weighting the components most sensitive to the parameters of interest, with no loss of information, taking into account information from the cross-correlation between the two probes. We derive optimal redshift weights which may be applied to individual galaxies when testing a given statistic and cosmological model.