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20122023
most citedThe COSMOS2015 Catalog: Exploring the 1<z<6 Universe with half a million galaxies

1.2k citations · 7.5k across the 81 of their papers we have counts for

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Showing 2019Show all

10 papers · 1 filter

astro-ph.GA2019★ 29 cited

Balmer Break Galaxy Candidates at : a Potential View on the Star-Formation Activity at

Ken Mawatari, Akio K. Inoue, Takuya Hashimoto +15

We search for galaxies with a strong Balmer break (Balmer Break Galaxies; BBGs) at over a 0.41 deg effective area in the COSMOS field. Based on rich imaging data, in…

astro-ph.GA2019★ 44 cited

X-shooter Spectroscopy and HST Imaging of 15 Ultra Massive Quiescent Galaxies at

Mikkel Stockmann, Sune Toft, Anna Gallazzi +23

We present a detailed analysis of a large sample of spectroscopically confirmed ultra-massive quiescent galaxies () at . This sam…

astro-ph.GA2019★ 100 cited

Automated Mining of the ALMA Archive in the COSMOS Field (A3COSMOS): I. Robust ALMA Continuum Photometry Catalogs and Stellar Mass and Star Formation Properties for ~700 Galaxies at z=0.5-6

Daizhong Liu, P. Lang, B. Magnelli +21

The rich information on (sub)millimeter dust continuum emission from distant galaxies in the public Atacama Large Millimeter/submillimeter Array (ALMA) archive is contained in thou…

astro-ph.GA2019★ 8 cited

Comparing Galaxy Clustering in Horizon-AGN Simulated Lightcone Mocks and VIDEO Observations

Peter Hatfield, Clotilde Laigle, Matt Jarvis +5

Hydrodynamical cosmological simulations have recently made great advances in reproducing galaxy mass assembly over cosmic time - as often quantified from the comparison of their pr…

astro-ph.GA2019★ 206 cited

Quiescent galaxies 1.5 billion years after the Big Bang and their progenitors

Francesco Valentino, Masayuki Tanaka, Iary Davidzon +18

We report two secure () and one tentative () spectroscopic confirmations of massive and quiescent galaxies through -band observations with Keck/MO…

astro-ph.GA2019★ 4 cited

Bringing manifold learning and dimensionality reduction to SED fitters

Shoubaneh Hemmati, Peter Capak, Milad Pourrahmani +8

We show unsupervised machine learning techniques are a valuable tool for both visualizing and computationally accelerating the estimation of galaxy physical properties from photome…