Machine learning forecasts of the cosmic distance duality relation with strongly lensed gravitational wave events

TL;DR

This paper demonstrates how machine learning methods, specifically Genetic Algorithms and Gaussian Processes, can be used with simulated gravitational wave data to test the cosmic distance duality relation in a model-independent way, achieving high precision.

Contribution

It introduces a novel application of machine learning techniques to reconstruct distances from simulated gravitational wave events for testing fundamental cosmological relations.

Findings

Both methods accurately recover the fiducial model.

Percent-level constraints are achievable at intermediate redshifts.

The approaches are effective with future Einstein Telescope data.

Abstract

We use simulated strongly lensed gravitational wave events from the Einstein Telescope to demonstrate how the luminosity and angular diameter distances, $d_L(z)$ and $d_A(z)$ respectively, can be combined to test in a model independent manner for deviations from the cosmic distance duality relation and the standard cosmological model. In particular, we use two machine learning approaches, the Genetic Algorithms and Gaussian Processes, to reconstruct the mock data and we show that both approaches are capable of correctly recovering the underlying fiducial model and can provide percent-level constraints at intermediate redshifts when applied to future Einstein Telescope data.