20 citations · 120 across the 44 of their papers we have counts for
30 papers · 1 filter
SHARP: Beyond JWST -- Revealing the galaxy birth and growth with the resolution of the ELT
P. Saracco, P. Conconi, C. Arcidiacono +37
A deep understanding of the life-cycle of galaxies, particularly those of high mass, requires clarifying the mechanisms that regulate star formation (SF) and its abrupt shutdown (q…
Archaeological investigation of galaxies' evolutionary history in the cosmic middle ages
Anna R. Gallazzi, Stefano Zibetti, Mark Sargent +14
The cosmic Middle Ages, spanning the last 8-10 Gyr of the Universe, is a critical period in which massive early-formed systems coexist with global star formation quenching in less…
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…
Euclid preparation: LXXXI. The impact of nonparametric star formation histories on spatially resolved galaxy property estimation using synthetic Euclid images
Euclid Collaboration, A. Nersesian, Abdurro'uf +283
We analyzed the spatially resolved and global star formation histories (SFHs) for a sample of 25 TNG50-SKIRT Atlas galaxies to assess the feasibility of reconstructing accurate SFH…
Euclid Quick Data Release (Q1). Searching for giant gravitational arcs in galaxy clusters with mask region-based convolutional neural networks
Euclid Collaboration, L. Bazzanini, G. Angora +306
Strong gravitational lensing (SL) by galaxy clusters is a powerful probe of their inner mass distribution and a key test bed for cosmological models. However, the detection of SL e…
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,…