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arXiv · 2005.04894

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
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Abstract

Green valley galaxies represent the population that is likely to transition from the star-forming to the 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 a 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 (sSFR) 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.

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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. 2020-05-11. Redshift Evolution of Green Valley Galaxies in Different Environments from the Hyper Suprime-Cam Survey. https://doi.org/10.3847/1538-4357%2Fab86a8

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