524 citations · 7.6k across the 128 of their papers we have counts for
32 papers · 1 filter
A Census from JWST of Extreme Emission Line Galaxies Spanning the Epoch of Reionization in CEERS
Kelcey Davis, Jonathan R. Trump, Raymond C. Simons +29
We present a sample of 1165 extreme emission-line galaxies (EELGs) at 4<z<9 selected using James Webb Space Telescope (JWST) NIRCam photometry in the Cosmic Evolution Early Release…
The Next Generation Deep Extragalactic Exploratory Public Near-Infrared Slitless Survey Epoch 1 (NGDEEP-NISS1): Extra-Galactic Star-formation and Active Galactic Nuclei at 0.5 < z < 3.6
Nor Pirzkal, Barry Rothberg, Casey Papovich +48
The Next Generation Deep Extragalactic Exploratory Public (NGDEEP) survey program was designed specifically to include Near Infrared Slitless Spectroscopic observations (NGDEEP-NIS…
Reliable Identification of Binary Supermassive Black Holes from Rubin Observatory Time-Domain Monitoring
Megan C. Davis, Kaylee E. Grace, Jonathan R. Trump +7
Periodic signatures in time-domain observations of quasars have been used to search for binary supermassive black holes. These searches, across existing time-domain surveys, have p…
Theoretical strong line metallicity diagnostics for the JWST era
Prerak Garg, Desika Narayanan, Ryan L. Sanders +5
The ratios of strong rest-frame optical emission lines are the dominant indicator of metallicities in high-redshift galaxies. Since typical strong-line based metallicity indicators…
The Complete CEERS Early Universe Galaxy Sample: A Surprisingly Slow Evolution of the Space Density of Bright Galaxies at z ~ 8.5-14.5
Steven L. Finkelstein, Gene C. K. Leung, Micaela B. Bagley +52
We present a sample of 88 candidate z~8.5-14.5 galaxies selected from the completed NIRCam imaging from the Cosmic Evolution Early Release Science (CEERS) survey. These data cover…
Galaxies Going Bananas: Inferring the 3D Geometry of High-Redshift Galaxies with JWST-CEERS
Viraj Pandya, Haowen Zhang, Marc Huertas-Company +53
The 3D geometry of high-redshift galaxies remains poorly understood. We build a differentiable Bayesian model and use Hamiltonian Monte Carlo to efficiently and robustly infer the…