activity
20152022
most citedThe FMOS-COSMOS survey of star-forming galaxies at VI: Redshift and emission-line catalog and basic properties of star-forming galaxies

101 citations · 333 across the 9 of their papers we have counts for

collaborators
Showing astro-ph.GAShow all

12 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.GA202237 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.GA202235 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.GA2019

The main sequence of star forming galaxies II. A non evolving slope at the high mass end

P. Popesso, L. Morselli, A. Concas +11

By using the deepest available mid and far infrared surveys in the CANDELS, GOODS and COSMOS fields we study the evolution of the Main Sequence (MS) of star forming galaxies (SFGs)…

astro-ph.GA2019

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

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…