activity
20082026
most citedThe COSMOS2015 Catalog: Exploring the 1<z<6 Universe with half a million galaxies

1.2k citations · 7.3k across the 64 of their papers we have counts for

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Showing 2022Show all

6 papers · 1 filter

astro-ph.GA2022

Evolution of Gas, and Star Formation from z = 0 to 5

Nick Scoville, Andreas Faisst, John Weaver +20

ALMA observations of the long wavelength dust continuum are used to estimate the gas masses in a sample of 708 star-forming (SF) galaxies at z = 0.3 to 4.5. We determine the depend…

astro-ph.GA2022★ 25 cited

COSMOS2020: Identification of High-z Protocluster Candidates in COSMOS

Malte Brinch, Thomas R. Greve, John R. Weaver +20

We conduct a systematic search for protocluster candidates at in the COSMOS field using the recently released COSMOS2020 source catalog. We select galaxies using a numbe…

astro-ph.GA2022★ 37 cited

Investigating the Effect of Galaxy Interactions on Star Formation at 0.5<z<3.0

Ekta A. Shah, Jeyhan S. Kartaltepe, Christina T. Magagnoli +24

Observations and simulations of interacting galaxies and mergers in the local universe have shown that interactions can significantly enhance the star formation rates (SFR) and fue…

astro-ph.GA2022★ 14 cited

A Machine Learning Approach to Predict Missing Flux Densities in Multi-band Galaxy Surveys

Nima Chartab, Bahram Mobasher, Asantha Cooray +21

We present a new method based on information theory to find the optimal number of bands required to measure the physical properties of galaxies with a desired accuracy. As a proof…

astro-ph.GA2022★ 35 cited

COSMOS2020: Ubiquitous AGN Activity of Massive Quiescent Galaxies at Revealed by X-ray and Radio Stacking

Kei Ito, Masayuki Tanaka, Takamitsu Miyaji +8

We characterize the average X-ray and radio properties of quiescent galaxies (QGs) with at . QGs are photometrically selected from the latest CO…

astro-ph.IM2022

Photometric Redshift Estimation with Convolutional Neural Networks and Galaxy Images: A Case Study of Resolving Biases in Data-Driven Methods

Q. Lin, D. Fouchez, J. Pasquet +4

Deep Learning models have been increasingly exploited in astrophysical studies, yet such data-driven algorithms are prone to producing biased outputs detrimental for subsequent ana…