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20202026
most citedEarly results from GLASS-JWST. XIV: A spectroscopically confirmed protocluster 650 million years after the Big Bang

123 citations · 493 across the 27 of their papers we have counts for

collaborators
Showing 2024 · astro-ph.GAShow all

5 papers · 2 filters

astro-ph.GA2024★ 14 cited

Early Results from GLASS-JWST. XXV. Electron Density in the Interstellar Medium at with NIRSpec High-resolution Spectroscopy

Sijia Li, Xin Wang, Yuguang Chen +17

The electron density () of the interstellar medium (ISM) in star-forming galaxies is intimately linked to star formation and ionization condition. Using the high-resol…

astro-ph.GA2024★ 1 cited

Not just PAH: why galaxies turn red in the Near-Infrared

Benedetta Vulcani, Tommaso Treu, Matthew Malkan +11

We measure the spectral properties of a sample of 20 galaxies at z~0.35 selected for having surprisingly red JWST/NIRCAM F200W-F444W colors. 19 galaxies were observed with JWST/NIR…

astro-ph.GA2024

BEACON: JWST NIRCam Pure-parallel Imaging Survey. I. Survey Design and Initial Results

Takahiro Morishita, Charlotte A. Mason, Kimi C. Kreilgaard +32

We introduce the Bias-free Extragalactic Analysis for Cosmic Origins with NIRCam (BEACON) survey, a JWST Cycle2 program allocated up to 600 pure-parallel hours of observations. BEA…

astro-ph.GA2024★ 14 cited

The rate and contribution of mergers to mass assembly from NIRCam observations of galaxy candidates up to 13.3 billion years ago

Nicolò Dalmasso, Antonello Calabrò, Nicha Leethochawalit +8

We present an analysis of the galaxy merger rate in the redshift range (i.e. about 1.5 to 0.5 Gyr after the Big Bang) based on visually identified galaxy mergers from m…

astro-ph.GA2024

A geostatistical analysis of multiscale metallicity variations in galaxies -- III. Spatial resolution and data quality limits

Benjamin Metha, Michele Trenti, Andrew Battisti +1

Geostatistical methods are powerful tools for understanding the spatial structure of the metallicity distribution of galaxies, and enable construction of accurate predictive models…