14 citations · 28 across the 5 of their papers we have counts for
5 papers
The promise of self-supervised and active learning for Strong Lens discovery: Astronomaly applied to KiDS
Margherita Grespan, Aprajita Verma, Michelle Lochner +3
Strong gravitational lenses (SGLs) are rare systems whose discovery currently relies primarily on supervised machine learning methods trained on large simulated datasets. We presen…
The Vera C. Rubin Observatory Data Preview 1
Vera C Rubin Observatory Team, Tatiana Acero Cuellar, Emily Acosta +325
We present Rubin Data Preview 1 DP1, the first data from the NSF DOE Vera C Rubin Observatory, comprising raw and calibrated single epoch images, coadds, difference images, detecti…
SLSim: a strong lensing population simulation package
Narayan Khadka, Simon Birrer, Henry Best +45
Gravitational lensing offers unique insights into cosmology by bending light around massive objects. Strong gravitational lensing, in particular, produces magnified and often multi…
The revolution in strong lensing discoveries from Euclid
Natalie E. P. Lines, Tian Li, Thomas E. Collett +5
Strong gravitational lensing offers a powerful and direct probe of dark matter, galaxy evolution and cosmology, yet strong lenses are rare: only 1 in roughly 10,000 massive galaxie…
A Bayesian Approach to Strong Lens Finding in the Era of Wide-area Surveys
Philip Holloway, Philip J. Marshall, Aprajita Verma +5
The arrival of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), Euclid-Wide and Roman wide area sensitive surveys will herald a new era in strong lens scienc…