Papers

The following papers use PyAutoLens:

Likelihood-free MCMC with Amortized Approximate Likelihood Ratios

Deep Learning the Morphology of Dark Matter Substructure

The molecular-gas properties in the gravitationally lensed merger HATLAS J142935.3-002836

Galaxy structure with strong gravitational lensing: decomposing the internal mass distribution of massive elliptical galaxies

Novel Substructure & Superfluid Dark Matter

CO, H2O, H2O+ line & dust emission in a z = 3.63 strongly lensed starburst merger at sub-kiloparsec scales

Subaru FOCAS IFU observations of two z=0.12 strong-lensing elliptical galaxies from SDSS MaNGA

MNELLS: The MUSE Nearby Early-Type Galaxy Lens Locator Survey

Decoding Dark Matter Substructure without Supervision

Auto-identification of unphysical source reconstructions in strong gravitational lens modelling

Strong lens modelling: comparing and combining Bayesian neural networks and parametric profile fitting

A forward-modelling method to infer the dark matter particle mass from strong gravitational lenses