Testing General Relativity using Large Scale Structures Photometric Redshift Surveys and Cosmic Microwave Background Lensing Effect
Shang Li, Jun-Qing Xia

TL;DR
This paper measures the $E_G$ statistic using DES photometric data and Planck CMB lensing to test General Relativity on large scales, employing neural networks and model-independent methods, and forecasts future constraints with upcoming surveys.
Contribution
It introduces a new approach to constrain the $E_G$ statistic with photometric redshift data, combining neural network reconstruction and model-independent galaxy bias measurements.
Findings
Measured $E_G$ at four redshifts consistent with $ m extLambda$CDM.
Demonstrated the potential for 1% precision with future surveys.
Validated the use of neural networks for growth rate estimation.
Abstract
The statistic provides a valuable tool for evaluating predictions of General Relativity (GR) by probing the relationship between gravitational potential and galaxy clustering on cosmological scales within the observable universe. In this study, we constrain the statistic using photometric redshift data from the Dark Energy Survey (DES) MagLim sample in combination with the Planck 2018 Cosmic Microwave Background (CMB) lensing map. Unlike spectroscopic redshift surveys, photometric redshift measurements are subject to significant redshift uncertainties, making it challenging to constrain the redshift distortion parameter with high precision. We adopt a new definition for this parameter, . In this formulation, we reconstruct the growth rate of structure, , using Artificial Neural Networks (ANN) method, while…
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Taxonomy
TopicsRelativity and Gravitational Theory · History and Developments in Astronomy · Cosmology and Gravitation Theories
