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. The galaxy redshift sample from the Dark Energy Spectroscopic Instrument (DESI) will have a significant overlap with major ongoing imaging surveys specifically designed for weak lensing measurements: the Kilo-Degree Survey (KiDS), the Dark Energy Survey (DES) and the Hyper Suprime-Cam (HSC) survey. In this work we analyse simulated redshift and lensing catalogues to establish a new strategy for combining high-quality cosmological imaging and spectroscopic data, in view of the first-year data assembly analysis of DESI. In a test case fitting for a reduced parameter set, we employ an optimal data compression scheme able to identify those aspects of the data that are most sensitive to the cosmological information, and amplify them with respect to other aspects of the data. We find this optimal compression approach is able to preserve all the information related to the growth of structure; we also extend this scheme to derive weights to be applied to individual galaxies, and show that these produce near-optimal results.
@article{arxiv.2208.01031,
title = {A data compression and optimal galaxy weights scheme for Dark Energy Spectroscopic Instrument and weak lensing datasets},
author = {Rossana Ruggeri and Chris Blake and Joseph DeRose and C. Garcia-Quintero and B. Hadzhiyska and M. Ishak and N. Jeffrey and S. Joudaki and Alex Krolewski and J. U. Lange and A. Leauthaud and A. Porredon and G. Rossi and C. Saulder and E. Xhakaj and 1 D. Brooks and G. Dhungana and A. de la Macorra and P. Doel and S. Gontcho A Gontcho and A. Kremin and M. Landriau and R. Miquel and 0 C. Poppett and F. Prada and Gregory Tarlé},
journal= {arXiv preprint arXiv:2208.01031},
year = {2023}
}