The following papers use PyAutoLens:

Dark Matter

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

Dark disk substructure and superfluid dark matter

Galaxy-galaxy strong lens perturbations: line-of-sight haloes versus lens subhaloes

Halo concentration strengthens dark matter constraints in galaxy–galaxy strong lensing analyses

Novel Substructure & Superfluid Dark Matter

Galaxy Formation & Evolution

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

Microlensing and the type Ia supernova iPTF16geu

Lens Modeling / Theory

Automated galaxy-galaxy strong lens modelling: no lens left behind

Systematic Errors Induced by the Elliptical Power-law model in Galaxy–Galaxy Strong Lens Modeling

Testing strong lensing subhalo detection with a cosmological simulation

Statistics / Machine Learning

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

Decoding Dark Matter Substructure without Supervision

Deep Learning the Morphology of Dark Matter Substructure

Domain Adaptation for Simulation-Based Dark Matter Searches Using Strong Gravitational Lensing

Likelihood-free MCMC with Amortized Approximate Likelihood Ratios

On machine learning search for gravitational lenses

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

Source Science

ALMA [{N} {II}] 205 μm Imaging Spectroscopy of the Lensed Submillimeter Galaxy ID 141 at Redshift 4.24

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

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

Modelling high-resolution ALMA observations of strongly lensed dusty star forming galaxies detected by Herschel


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

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