5 citations · 16 across the 65 of their papers we have counts for
94 papers · 1 filter
Beyond Stage IV: Quasar and Galaxy Clustering and the Fundamental Physics of the 2040s
M. Guidi, M. Moresco, H. K. Herrera-Alcantar +24
Stage IV galaxy surveys (DESI, 4MOST, MOONS, Euclid) are establishing precision constraints on cosmological parameters through baryon acoustic oscillations and redshift-space disto…
Euclid: Photometric redshift calibration with self-organising maps
W. Roster, A. H. Wright, H. Hildebrandt +173
The Euclid survey aims to trace the evolution of cosmic structures up to redshift 3 and beyond. Its success depends critically on obtaining highly accurate mean redshift…
Euclid Quick Data Release (Q1): VIS processing and data products
Euclid Collaboration, H. J. McCracken, K. Benson +377
This paper describes the VIS Processing Function (VIS PF) of the Euclid ground segment pipeline, which processes and calibrates raw data from the VIS camera. We present the algorit…
Euclid preparation. Review of forecast constraints on dark energy and modified gravity
Euclid Collaboration, N. Frusciante, M. Martinelli +326
The Euclid mission has been designed to provide, as one of its main deliverables, information on the nature of the gravitational interaction, which determines the expansion of the…
Euclid: Quick Data Release (Q1) -- A photometric search for ultracool dwarfs in the Euclid Deep Fields
M. Žerjal, C. Dominguez-Tagle, N. Vitas +178
We present a catalogue of 5306 new ultracool dwarf (UCD) candidates in the three Euclid Deep Fields in the Q1 data release. They range from late M to late T dwarfs, and include 120…
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…