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A data compression and optimal galaxy weights scheme for Dark Energy Spectroscopic Instrument and weak lensing data sets

Ruggeri et al. (2023)

Christoph Saulder #14 Co-author
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY IF 5.235 A 등급 International collaboration

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. 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 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 structures.

AI Classification

Maturity Tier

Tier 1 — Classical ML

AI Role

data-reduction / preprocessing

supporting

Techniques

classical-ML

Tasks

dimensionality-reduction

Subfield

cosmology

tabular-catalog

Flags

Develops AI method
· Classification confidence: 70%