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This paper introduces a novel method for generating joint posterior samples of source galaxies and foreground mass distributions in strong gravitational lensing scenarios, leveraging diffusion models and recurrent inference machines. By addressing the challenges posed by high-dimensional and non-linear representations, the approach effectively models realistic gravitational lensing simulations with high fidelity. The key result demonstrates that this method can accurately infer brightness and mass distributions from high-resolution, high signal-to-noise observations, significantly improving upon traditional and machine learning techniques.
Achieving high-fidelity inference in gravitational lensing, this method reveals intricate details of galaxy mass distributions that were previously obscured by noise.
Modeling galaxy-galaxy strong gravitational lenses to infer the brightness of the source galaxy and the mass distribution of the foreground galaxy is computationally challenging, particularly for high-resolution, high signal-to-noise ratio observations. In this regime, high-dimensional representations of both the source and the foreground mass distribution are necessary to model the data down to the noise level. This inference problem has been challenging for both traditional and machine learning-based methods because of its high dimensionality and its non-linearity in the foreground mass distribution. We present a method to generate joint posterior samples of the source galaxy and foreground mass distribution as pixelated images conditioned on observations. The method combines diffusion-based generative modeling and recurrent inference machines. It can model realistic gravitational lensing simulations with background and foreground galaxies drawn from cosmological hydrodynamical simulations down to the noise level.
Mila