9 citations · 22 across the 8 of their papers we have counts for
13 papers
Euclid preparation. Galaxy 2-point correlation function modelling in redshift space
Euclid Collaboration, M. Kärcher, M. -A. Breton +309
The Euclid satellite will measure spectroscopic redshifts for tens of millions of emission-line galaxies. In the context of Stage-IV surveys, the 3-dimensional clustering of galaxi…
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: A machine-learning search for dual and lensed AGN at sub-arcsec separations
L. Ulivi, F. Mannucci, M. Scialpi +189
Cosmological models of hierarchical structure formation predict the existence of a widespread population of dual accreting supermassive black holes (SMBHs) on kpc-scale separations…
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 first catalogue of strong-lensing galaxy clusters
Euclid Collaboration, P. Bergamini, M. Meneghetti +375
We present the first catalogue of strong lensing galaxy clusters identified in the Euclid Quick Release 1 observations (covering ). This catalogue is the resu…