20 citations · 20 across the 4 of their papers we have counts for
5 papers
Euclid preparation. CosmoPostProcess: A simulation calibrated framework for weak lensing selection bias in richness-selected galaxy clusters
Euclid Collaboration, R. Ingrao, M. Costanzi +280
We present \texttt{CosmoPostProcess}, a simulation-based forward-modelling algorithm calibrated to reproduce Euclid optical cluster observables. Its main deliverable is a correctio…
Euclid Quick Data Release (Q1). AstroVink: A vision transformer approach to find strong gravitational lens systems
Euclid Collaboration, S. H. Vincken, K. Rojas +302
We present AstroVink, a vision transformer classifier designed for automated identification of strong lens candidates in Euclid imaging. We build upon the DINOv2 encoder, fine tune…
Euclid Quick Data Release (Q1). AgileLens: A scalable CNN-based pipeline for strong gravitational lens identification
Euclid Collaboration, X. Xu, R. Chen +308
We present an end-to-end, iterative pipeline for efficient identification of strong galaxy--galaxy lensing systems, applied to the Euclid Q1 imaging data. Starting from VIS catalog…
Discovering Strong Gravitational Lenses in the Dark Energy Survey with Interactive Machine Learning and Crowd-sourced Inspection with Space Warps
J. Gonzalez, P. Holloway, T. Collett +70
We conduct a search for strong gravitational lenses in the Dark Energy Survey (DES) Year 6 imaging data. We implement a pre-trained Vision Transformer (ViT) for our machine learnin…
The impact of human expert visual inspection on the discovery of strong gravitational lenses
Karina Rojas, Thomas E. Collett, Daniel Ballard +53
We investigate the ability of human 'expert' classifiers to identify strong gravitational lens candidates in Dark Energy Survey like imaging. We recruited a total of 55 people that…