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Journal Articles The Astrophysical Journal Year : 2022

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

A. Gu
  • Function : Author
X. Huang
  • Function : Author
W. Sheu
  • Function : Author
G. Aldering
  • Function : Author
A. S. Bolton
  • Function : Author
K. Boone
  • Function : Author
A. Dey
  • Function : Author
A. Filipp
  • Function : Author
S. Perlmutter
  • Function : Author
D. Rubin
  • Function : Author
E. F. Schlafly
  • Function : Author
D. J. Schlegel
  • Function : Author
Y. Shu
  • Function : Author
S. H. Suyu
  • Function : Author


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 multistart 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 s. 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.
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Dates and versions

insu-03780754 , version 1 (20-09-2022)





A. Gu, X. Huang, W. Sheu, G. Aldering, A. S. Bolton, et al.. GIGA-Lens: Fast Bayesian Inference for Strong Gravitational Lens Modeling. The Astrophysical Journal, 2022, 935, ⟨10.3847/1538-4357/ac6de4⟩. ⟨insu-03780754⟩
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