550 citations · 650 across the 17 of their papers we have counts for
13 papers · 1 filter
Euclid Quick Data Release (Q1). Searching for radio-selected \Euclid-dark galaxies in the EDF-N
Euclid Collaboration, M. Giulietti, I. Prandoni +308
We present and investigate the properties of a sample of radio-selected, Euclid-dark galaxies, identified from LOFAR HBA observations at 144 MHz within the Euclid Deep Field-North…
The MUSE-Faint survey V. The binary fraction of Leo T
Daniel Vaz, Jarle Brinchmann, Sebastian Kamann +3
The Leo T dwarf galaxy, the faintest and least massive galaxy known to have recent star formation (), exhibits a high dynamical mass-to-light ratio based on its stellar…
Euclid Quick Data Release (Q1): Euclid spectroscopy of quasars. 1. Identification and redshift determination of 3500 bright quasars
Euclid Collaboration, Y. Fu, R. Bouwens +354
The slitless spectroscopy mode of the NISP onboard Euclid has enabled efficient spectroscopy of objects within a large FoV. We present a large and homogeneous sample of bright quas…
Euclid Quick Data Release (Q1). Spectroscopic unveiling of highly ionised lines at z = 2.48-3.88
Euclid Collaboration, D. Vergani, S. Quai +316
This study explores a rare population of sources in a currently uncharted region of spectroscopic redshift space in the Euclid Quick Data Release (Q1), and is intended potentially…
A gradient boosting and broadband approach to finding Lyman-α emitting galaxies beyond narrowband surveys
A. Vale, A. Paulino-Afonso, A. Humphrey +5
In this work, we test whether gradient-boosting algorithms, trained on broadband photometric data from traditional Lyman- emitting (LAE) surveys, can efficiently and accurately…
Euclid preparation. Estimating galaxy physical properties using CatBoost chained regressors with attention
Euclid Collaboration, A. Humphrey, P. A. C. Cunha +232
Euclid will image ~14000 deg^2 of the extragalactic sky at visible and NIR wavelengths, providing a dataset of unprecedented size and richness that will facilitate a multitude of s…
Euclid Quick Data Release (Q1) First study of red quasars selection
Euclid Collaboration, F. Tarsitano, S. Fotopoulou +322
Red quasars constitute an important but elusive phase in the evolution of supermassive black holes, where dust obscuration can significantly alter their observed properties. They h…
Euclid Quick Data Release (Q1) Exploring galaxy properties with a multi-modal foundation model
Euclid Collaboration, M. Siudek, M. Huertas-Company +321
Modern astronomical surveys, such as the Euclid mission, produce high-dimensional, multi-modal data sets that include imaging and spectroscopic information for millions of galaxies…
Exploring the physical properties of Type II Quasar candidates at intermediate redshifts with CIGALE
P. A. C. Cunha, A. Humphrey, J. Brinchmann +4
Active Galactic Nuclei (AGN) significantly influence galaxy evolution. Specific sources such as obscured AGNs, especially Type II quasars (QSO2), still remain understudied. We char…
Euclid preparation. XLIX. Selecting active galactic nuclei using observed colours
Euclid Collaboration, L. Bisigello, M. Massimo +244
Euclid will cover over 14000 with two optical and near-infrared spectro-photometric instruments, and is expected to detect around ten million active galactic nuclei (AGN)…
Euclid preparation. LI. Forecasting the recovery of galaxy physical properties and their relations with template-fitting and machine-learning methods
Euclid Collaboration, A. Enia, M. Bolzonella +260
Euclid will collect an enormous amount of data during the mission's lifetime, observing billions of galaxies in the extragalactic sky. Along with traditional template-fitting metho…
Selection of powerful radio galaxies with machine learning
R. Carvajal, I. Matute, J. Afonso +12
We developed and trained a pipeline of three machine learning (ML) models than can predict which sources are more likely to be an AGN and to be detected in specific radio surveys.…
Improving machine learning-derived photometric redshifts and physical property estimates using unlabelled observations
A. Humphrey, P. A. C. Cunha, A. Paulino-Afonso +5
In the era of huge astronomical surveys, machine learning offers promising solutions for the efficient estimation of galaxy properties. The traditional, `supervised' paradigm for t…