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Improving Lens Modelling from Ground-Based Imaging with Deconvolution
Eric Paic, Kenneth C. Wong, Anupreeta More +1
Accurate and precise mass modelling of strong lensing systems is essential for extracting insights into cosmology, galaxy environments, and the nature of dark matter. The Hyper Sup…
Does Machine Learning Work? A Comparative Analysis of Strong Gravitational Lens Searches in the Dark Energy Survey
J. Gonzalez, T. Collett, K. Rojas +9
We present a systematic comparison of three independent machine learning (ML)-based searches for strong gravitational lenses applied to the Dark Energy Survey (Jacobs et al. 2019a,…
Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine E -- Ensemble classification of strong gravitational lenses: lessons for Data Release 1
Euclid Collaboration, P. Holloway, A. Verma +343
The Euclid Wide Survey (EWS) is expected to identify of order galaxy-galaxy strong lenses across deg. The Euclid Quick Data Release (Q1) of deg Eu…
Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine D -- Double-source-plane lens candidates
Euclid Collaboration, T. Li, T. E. Collett +335
Strong gravitational lensing systems with multiple source planes are powerful tools for probing the density profiles and dark matter substructure of the galaxies. The ratio of Eins…
Euclid Quick Data Release (Q1): The Strong Lensing Discovery Engine A -- System overview and lens catalogue
Euclid Collaboration, M. Walmsley, P. Holloway +372
We present a catalogue of 497 galaxy-galaxy strong lenses in the Euclid Quick Release 1 data (63 deg). In the initial 0.45\% of Euclid's surveys, we double the total number of…
Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine C: Finding lenses with machine learning
Euclid Collaboration, N. E. P. Lines, T. E. Collett +350
Strong gravitational lensing has the potential to provide a powerful probe of astrophysics and cosmology, but fewer than 1000 strong lenses have been confirmed so far. With a 0.16'…