Robust Strong Lensing Time Delay Estimation

TL;DR

This paper introduces a Gaussian process-based method for accurately estimating time delays in strong gravitational lensing systems, addressing challenges like noise, gaps, and microlensing effects to improve cosmological measurements.

Contribution

The paper presents a novel application of Gaussian processes for robust, blind reconstruction of time delays in lensed systems, handling real-world data complexities.

Findings

Successful blind reconstruction of time delays from real data.

Reduction in uncertainty of time delay estimates.

Effective handling of noise, gaps, and microlensing effects.

Abstract

Strong gravitational lensing of time variable sources such as quasars and supernovae creates observable time delays between the multiple images. Time delays can provide a powerful cosmographic probe through the "time delay distance" involving the ratio of lens, source, and lens-source distances. However, lightcurves of lensed images have measurement gaps, noise, systematics such as microlensing from substructure along an image line of sight, and no a priori functional model, making robust time delay estimation challenging. Using Gaussian process techniques, we demonstrate success in accurate blind reconstruction of time delays and reduction in uncertainties for real data.