GIGA-Lens: Fast Bayesian Inference for Strong Gravitational Lens Modeling

TL;DR

GIGA-Lens is a GPU-accelerated Bayesian framework that enables rapid and scalable modeling of strong gravitational lensing systems, crucial for upcoming large-scale astronomical surveys.

Contribution

It introduces a novel, GPU-accelerated Bayesian inference pipeline for strong lens modeling, combining optimization, variational inference, and Hamiltonian Monte Carlo with automatic differentiation.

Findings

Models a single system in 105 seconds on four GPUs.

Demonstrates high performance and scalability on simulated data.

Suitable for large surveys with up to 10^5 lensing systems.

Abstract

We present GIGA-Lens: a gradient-informed, GPU-accelerated Bayesian framework for modeling strong gravitational lensing systems, implemented in TensorFlow and JAX. The three components, optimization using multi-start gradient descent, posterior covariance estimation with variational inference, and sampling via Hamiltonian Monte Carlo, all take advantage of gradient information through automatic differentiation and massive parallelization on graphics processing units (GPUs). We test our pipeline on a large set of simulated systems and demonstrate in detail its high level of performance. The average time to model a single system on four Nvidia A100 GPUs is 105 seconds. The robustness, speed, and scalability offered by this framework make it possible to model the large number of strong lenses found in current surveys and present a very promising prospect for the modeling of $\mathcal{O}(10^5)$ lensing systems expected to be discovered in the era of the Vera C. Rubin Observatory, Euclid, and the Nancy Grace Roman Space Telescope.