Redshift Evolution of Green Valley Galaxies in Different Environments from the Hyper Suprime-Cam Survey

Hung Yu Jian, Lihwai Lin, Yusei Koyama, Ichi Tanaka, Keiichi Umetsu, Bau Ching Hsieh, Yuichi Higuchi, Masamune Oguri, Surhud More, Yutaka Komiyama, Tadayuki Kodama, Atsushi J. Nishizawa, Yu Yen Chang

Research output: Contribution to journalArticlepeer-review

Abstract

Green valley galaxies represent the population that is likely to transition from star-forming to quiescent phases. To investigate the role of the environment in quenching star formation, we use the wide-field data from the Hyper Suprime-Cam Strategic Subaru Proposal survey to quantify the frequency of green valley galaxies in different environments and their redshift evolution. We find that the green valley fraction, in general, is less than 20% in any redshift and environment. The green valley fraction, when normalized to the total population, is higher in the field than that in groups or clusters and decreases with decreasing redshift and increasing mass. The lower fraction of transitional galaxies in denser environments could be a consequence of the lack of star-forming galaxies, which could be the progenitors of green valley galaxies. To assess the effect of the environment on star formation quenching, we define the effective green valley fraction as the ratio of the number of green valley galaxies to that of nonquiescent galaxies only. The effective green valley fraction for field galaxies is lower than that for group or cluster galaxies, which reveals a strong positive mass dependence and mild redshift evolution. Moreover, the specific star formation rate is reduced by 0.1-0.3 dex in groups or clusters. Our results thus imply that an ongoing slow quenching process has been acting in the dense environment since z ∼ 1.

Original languageEnglish
Article number125
JournalAstrophysical Journal
Volume894
Issue number2
DOIs
Publication statusPublished - 2020 May 10

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

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