Kernel regression estimates of time delays between gravitationally lensed fluxes

TL;DR

This paper introduces a kernel regression-based method for accurately estimating time delays in gravitationally lensed quasars, demonstrating its effectiveness on both simulated and real data.

Contribution

The paper presents a novel kernel regression approach for estimating time delays from multiple datasets of the same quasar, improving measurement accuracy.

Findings

The method performs well on artificial datasets in controlled experiments.

It provides estimates consistent with existing results on real quasar data.

Kernel regression offers a flexible and precise tool for cosmological measurements.

Abstract

Strongly lensed variable quasars can serve as precise cosmological probes, provided that time delays between the image fluxes can be accurately measured. A number of methods have been proposed to address this problem. In this paper, we explore in detail a new approach based on kernel regression estimates, which is able to estimate a single time delay given several datasets for the same quasar. We develop realistic artificial data sets in order to carry out controlled experiments to test of performance of this new approach. We also test our method on real data from strongly lensed quasar Q0957+561 and compare our estimates against existing results.