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20062023
most citedCEERS Key Paper I: An Early Look into the First 500 Myr of Galaxy Formation with JWST

524 citations · 11.6k across the 138 of their papers we have counts for

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Showing 2019Show all

6 papers · 1 filter

astro-ph.GA2019★ 28 cited

The host galaxies of z=7 quasars: predictions from the BlueTides simulation

Madeline A. Marshall, Yueying Ni, Tiziana Di Matteo +4

We examine the properties of the host galaxies of quasars using the large volume, cosmological hydrodynamical simulation BlueTides. We find that the 10 most massive black hol…

astro-ph.GA2019★ 46 cited

Recalibrating the Cosmic Star Formation History

Stephen M. Wilkins, Christopher C. Lovell, Elizabeth R. Stanway

The calibrations linking observed luminosities to the star formation rate depend on the assumed stellar population synthesis model, initial mass function, star formation and metal…

astro-ph.CO2019★ 39 cited

Cosmic variance of galaxies: Prediction from BlueTides

Aklant K. Bhowmick, Rachel S. Somerville, Tiziana DiMatteo +3

In the coming decade, a new generation of telescopes, including JWST and WFIRST, will probe the period of the formation of first galaxies and quasars, and open up the last frontier…

astro-ph.GA2019★ 39 cited

Nebular Line Emission During the Epoch of Reionization

Stephen M. Wilkins, Christopher C. Lovell, Ciaran Fairhurst +6

Nebular emission lines associated with galactic HII regions carry information about both physical properties of the ionised gas and the source of ionising photons as well as provid…

astro-ph.GA2019★ 96 cited

Detailed dust modelling in the L-Galaxies semi-analytic model of galaxy formation

Aswin P. Vijayan, Scott J. Clay, Peter A. Thomas +3

We implement a detailed dust model into the L-Galaxies semi-analytical model which includes: injection of dust by type II and type Ia supernovae (SNe) and AGB stars; grain growth i…

astro-ph.GA2019★ 50 cited

Learning the Relationship between Galaxies Spectra and their Star Formation Histories using Convolutional Neural Networks and Cosmological Simulations

Christopher C. Lovell, Viviana Acquaviva, Peter A. Thomas +3

We present a new method for inferring galaxy star formation histories (SFH) using machine learning methods coupled with two cosmological hydrodynamic simulations. We train Convolut…