9 citations · 17 across the 5 of their papers we have counts for
10 papers
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). From simulations to sky: Advancing machine-learning lens detection with real Euclid data
Euclid Collaboration, N. E. P. Lines, T. E. Collett +301
In the era of large-scale surveys like Euclid, machine learning has become an essential tool for identifying rare yet scientifically valuable objects, such as strong gravitational…
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: Discovery of bright Lyman-break galaxies in UltraVISTA and Euclid COSMOS
R. G. Varadaraj, R. A. A. Bowler, M. J. Jarvis +163
We present a search for Lyman-break galaxies using the near-infrared UltraVISTA survey in the COSMOS field, reaching depths in of 26.2. W…
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…
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…