Lensing Code

Short Description

One of our goals is to keep developing lens modelling codes targeted at high angular resolution observations. We strive to continually improve our approach to include an increasing number of physical effects and target an increasing variety of astrophysical questions. At the same time, we make use of Machine Learning approaches to quantify sources of systematic errors in lens modelling and extract valuable information from large samples of gravitational lens systems. For more details, see the list of publications below and here. If you are interested in using our methodology, get in touch.


Angular complexity in strong lens substructure detection.
O' Riordan et al., submitted to MNRAS

Selection functions of strong lens finding neural networks.
Herle et al., submitted to MNRAS

Sensitivity of strong lensing observations to dark matter substructure: a case study with Euclid.
O' Riordan et al. (2023), MNRAS, 521, 2342

Realistic galaxy images and improved robustness in machine learning tasks from generative modelling.
Holzschuh et al. (2022), MNRAS, 515, 652

A lensed radio jet at milli-arcsecond resolution - II. Constraints on fuzzy dark matter from an extended gravitational arc.
Powell et. al (2023), MNRAS, 524, 84

A novel approach to visibility-space modelling of interferometric gravitational lens observations at high angular resolution.
Powell et. al (2021), MNRAS, 501, 515

Resolving on 100 pc scales the UV-continuum in Lyman-α emitters between redshift 2 and 3 with gravitational lensing.
Ritondale et al. (2019), MNRAS, 482, 4744

A novel 3D technique to study the kinematics of lensed galaxies.
Rizzo et al. (2018), MNRAS, 481, 5606

ALMA imaging of SDP.81 - I. A pixelated reconstruction of the far-infrared continuum emission.
Rybak et al. (2015), MNRAS, 415, 40

Bayesian strong gravitational-lens modelling on adaptive grids: objective detection of mass substructure in galaxies.
Vegetti & Koopmans (2009), MNRAS, 392, 945