activity
20242026
most citedEuclid: A complete Einstein ring in NGC 6505

15 citations · 45 across the 15 of their papers we have counts for

collaborators

29 papers

astro-ph.GA2026

Euclid: Early Release Observations -- The star-formation history of massive early-type galaxies in the Perseus cluster

S. Martocchia, A. Boselli, J. -C. Cuillandre +157

The Euclid Early Release Observations (ERO) programme targeted the Perseus galaxy cluster in its central region over 0.7deg. We combined the exceptional image quality and depth…

astro-ph.IM2025

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…

astro-ph.GA2025

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…

astro-ph.IM2025

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…

astro-ph.GA2025

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…

astro-ph.GA2025

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,…