Multi-purpose lens modeling software package
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Updated
Sep 7, 2026 - Python
Multi-purpose lens modeling software package
A pipeline for versatile strong lens sample simulations
Automated pipeline for lens modelling based on lenstronomy
Implicit neural representations for strong lensing source reconstruction. Code repository associated with https://arxiv.org/abs/2206.14820.
Generates the training data for hierarchical inference of strongly-lensed systems with Bayesian neural networks
Deep-learning classification of strong gravitational lenses for the LSST Strong Lensing Data Challenge (2025): Vision Transformer / ConvNeXt backbones on 5-band (grizy)
Code to provide approximate magnification probability distributions under microlensing by compact objects such as stars or PBHs of strongly lensed stars.
slcomp: compilation of strong lensing objects
Contains the COLAB notebook and datasets for Gravity Resonance Threshold Theory paper.
Code for "Measuring the substructure mass power spectrum of 23 SLACS strong galaxy-galaxy lenses with convolutional neural networks"
Gravity Threshold Theory Strong Lensing using CAT, SHARON and GLAFIC
Code to simulate mock strong lensed galaxies
Temporal-Spatial Coupling in Gravitational Lensing: A Reinterpretation of Dark Matter Observations — Reinterprets dark matter observations as phantom mass from temporal shear in gravitational lensing. Conformal metric coupling generates gravitational shear signatures, resolving strong lensing time delays and the core-cusp problem.
Constrain curvature with strong lenses and complementary probes
Searching for galaxy satellites by their impact on lens potential in strong gravitational lensing setup
Simone La Porta's Master thesis in gravitational lensing with applications of automatic differentiation @ Alma Mater Studiorum - Università di Bologna
Temporal Equivalence Principle: A Blind-Prediction Residual Test in Multiply-Imaged Supernovae — Gravitational lensing and time-delay cosmography tests of TEP using COSMOGRAIL, SH0ES, and H0LiCOW data, with blind-prediction residual analysis of multiply-imaged supernovae.
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