Papers#
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
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
Source Science
The molecular-gas properties in the gravitationally lensed merger HATLAS J142935.3-002836
Surveys
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