Speeding up Gravitational Lens Mass Models with Machine Learning: Applications in X-ray Astronomy

arXiv:2607.22812 · astro-ph.GA, astro-ph.CO, astro-ph.HE · Submitted 2026-07-24 · Read on arXiv

Alex Ostridge, Rafael Martínez-Galarza, Júlia M. Sisk-Reynés, Daniel A. Schwartz, Anna Barnacka

astro-ph.GA, astro-ph.CO, astro-ph.HE

Submitted: 2026-07-24

Comments: Accepted for publication in MNRAS on 21 July 2026. 16 pages, 9 figures, 4 tables. The full code pipeline to reproduce the simulations, neural network training and parameter predictions, along with the trained network models and optimisation code, are publicly available on GitHub at https://github.com/aostridge/Grav-Lens-ML. Comments are welcome!

Code: https://github.com/aostridge/Grav-Lens-ML

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 29× in this paper · explained in 1 episode
• AGN — 25× in this paper · explained in 1 episode
• Loss Function — 14× in this paper · explained in 2 episodes
• Activation Function — 9× in this paper · explained in 1 episode
• Gravitational Lensing — 9× in this paper · explained in 2 episodes
• Microlensing — 9× in this paper · explained in 2 episodes
• Fidelity — 7× in this paper · explained in 2 episodes
• Singular Isothermal Ellipsoid — 6× in this paper
• Degeneracy — 3× in this paper · explained in 3 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• AGN Feedback — 1× in this paper · explained in 1 episode
• Active Galactic Nuclei (AGN) — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Bayesian Framework — 1× in this paper · explained in 3 episodes
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Hubble Space Telescope — 1× in this paper
• Initial Data — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Velocity Dispersion — 1× in this paper · explained in 2 episodes

Sources

• Searching for strong lensing by late-type galaxies in UNIONS
• Lens Modeling of STRIDES Strongly Lensed Quasars using Neural Posterior Estimation
• Euclid Quick Data Release (Q1): The Strong Lensing Discovery Engine A -- System overview and lens catalogue
• Euclid Quick Data Release (Q1) The Strong Lensing Discovery Engine B -- Early strong lens candidates from visual inspection of high velocity dispersion galaxies
• Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine C: Finding lenses with machine learning
• Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine D -- Double-source-plane lens candidates
• Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine E -- Ensemble classification of strong gravitational lenses: lessons for Data Release 1
• Strong Gravitational Lensing Parameter Estimation with Vision Transformer
• A Catalog of Mass Models for Gravitational Lensing
• An overview of gradient descent optimization algorithms
• Gradient Descent-Type Methods: Background and Simple Unified Convergence Analysis
• POLISH'ing the Sky: Wide-Field and High-Dynamic Range Interferometric Image Reconstruction with Application to Strong Lens Discovery

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