Hierarchical Bayesian scheme for measuring the properties of dark energy with Strong gravitational lensing

TL;DR

This paper develops a hierarchical Bayesian framework to analyze strong gravitational lensing, specifically double-source plane systems, to improve measurements of dark energy properties in the universe.

Contribution

It introduces a novel hierarchical Bayesian scheme for analyzing double-source plane lens systems to better constrain dark energy parameters.

Findings

Double lenses are promising for constraining cosmological parameters.

Bayesian models effectively analyze artificially generated datasets.

Hierarchical approach improves measurement accuracy of dark energy properties.

Abstract

Current sky surveys have been conducted very accurately in order to understand our universe. One of the phenomena survey maps provide is gravitational effect. Albert Einstein (1936) first discussed the possibilities of gravitational lensing effects based on his theory of general relativity, which might give us some information about cosmology. We often categorize the effects by micro, weak, and strong lensing types. This paper focuses on the strong gravitational lensing, especially the double-source plane lens systems. It was recently recognized that the double lensing events can provide us with another method for constraining the cosmological parameters. To get a general idea of 'how many of these lensing events can be found', we set up a toy model, then follow up with several Bayesian models in order to perform statistical analysis with artificially drawn datasets. The results showed that the double lenses are, in fact, promising objects to constrain the cosmological parameters.