A differentiable binary microlensing model using adaptive contour integration method

TL;DR

This paper introduces microlux, a Jax-based, differentiable binary microlensing model that efficiently computes light curves and derivatives, enabling advanced gradient-based analysis for microlensing event modeling.

Contribution

The paper presents microlux, a novel differentiable microlensing modeling tool that incorporates an adaptive contour integration method for accurate and efficient computation of light curves and derivatives.

Findings

Microlux accurately models binary microlensing across relevant parameters.

It enables gradient-based posterior estimation methods like HMC.

The code's efficiency and accuracy are verified through tests.

Abstract

We present microlux, which is a Jax-based code that can compute the binary microlensing light curve and its derivatives both efficiently and accurately. The key feature of microlux is the implementation of a modified version of the adaptive sampling algorithm that was originally proposed by V. Bozza to account for the finite-source effect most efficiently. The efficiency and accuracy of microlux have been verified across the relevant parameter space for binary microlensing. As a differentiable code, microlux makes it possible to apply gradient-based algorithms to the search and posterior estimation of the microlensing modeling. As an example, we use microlux to model a real microlensing event and infer the model posterior via both Fisher information matrix and Hamiltonian Monte Carlo, neither of which would have been possible without the access to accurate model gradients.