Cosmological Tests With Strong Gravitational Lenses using Gaussian Processes
Manoj K. Yennapureddy, Fulvio Melia

TL;DR
This study employs Gaussian Processes to analyze strong gravitational lens data, aiming to improve cosmological model comparisons by reducing uncertainties and enhancing the ability to distinguish between the standard model and alternative theories.
Contribution
It introduces a non-parametric Gaussian Process method for analyzing lensing data, improving model discrimination over traditional parametric fits.
Findings
GP approach increases model comparison probability differences to 10-30%
Results favor R_h=ct universe slightly over LCDM
Method will be more powerful with larger future lens samples
Abstract
Strong gravitational lenses provide source/lens distance ratios D_obs useful in cosmological tests. Previously, a catalog of 69 such systems was used in a one-on-one comparison between the standard model, LCDM, and the R_h=ct universe, which has thus far been favored by the application of model selection tools to many other kinds of data. But in that work, the use of model parametric fits to the observations could not easily distinguish between these two cosmologies, in part due to the limited measurement precision. Here, we instead use recently developed methods based on Gaussian Processes (GP), in which D_obs may be reconstructed directly from the data without assuming any parametric form. This approach not only smooths out the reconstructed function representing the data, but also reduces the size of the 1-sigma confidence regions, thereby providing greater power to discern between…
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