Computational Methods for Gravitational Lensing

TL;DR

This paper introduces new computational algorithms and techniques for modeling strong gravitational lensing, enabling the use of complex mass distributions and diverse observational data to improve lens models.

Contribution

It presents a novel algorithm for solving the lens equation for arbitrary mass distributions and evaluates methods for constraining models using various observational data, implemented in the gravlens software.

Findings

New algorithm allows complex lens modeling.

Techniques effectively utilize diverse observational data.

Software gravlens is user-friendly and publicly available.

Abstract

Modern applications of strong gravitational lensing require the ability to use precise and varied observational data to constrain complex lens models. I discuss two sets of computational methods for lensing calculations. The first is a new algorithm for solving the lens equation for general mass distributions. This algorithm makes it possible to apply arbitrarily complicated models to observed lenses. The second is an evaluation of techniques for using observational data including positions, fluxes, and time delays of point-like images, as well as maps of extended images, to constrain models of strong lenses. The techniques presented here are implemented in a flexible and user-friendly software package called gravlens, which is made available to the community.