550 citations · 3.6k across the 121 of their papers we have counts for
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Super-resolving Herschel - a deep learning based deconvolution and denoising technique
Dennis Koopmans, Lingyu Wang, Berta Margalef-Bentabol +9
Dusty star-forming galaxies (DSFGs) dominate the far-infrared and sub-millimetre number counts, but single-dish surveys suffer from poor angular resolution, complicating mult-wavel…
HELM's deep: Highly Extincted Low-Mass galaxies seen by JWST
L. Bisigello, G. Gandolfi, A. Grazian +25
The dust content of star-forming galaxies is generally positively correlated with their stellar mass. However, some recent JWST studies have shown the existence of a population of…
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: The first statistical census of dusty and massive objects in the ERO/Perseus field
G. Girardi, A. Grazian, G. Rodighiero +175
Our comprehension of the history of star formation at relies on rest-frame UV observations, yet this selection misses the most dusty and massive sources, yielding an incomple…
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…
The observed total star formation rate function up to z \sim 6: complementary UV and IR contributions and comparison with state-of-the-art galaxy formation models
A. Traina, C. Gruppioni, I. Delvecchio +21
We investigate how the obscured IR-derived and the dust-corrected UV star formation rate functions (SFRFs) compare with each other, and with predictions from state-of-the-art theor…