The ALMaQUEST Survey XII: Dense Molecular Gas As Traced by HCN and HCO^+ in Green Valley Galaxies
The Astrophysical Journal(2024)
Acad Sinica | Tamkang Univ | Univ Victoria | Natl Astron Observ Japan | Observ Astron Nacl IGN | Univ Illinois | Univ Cambridge | Grad Inst Adv Studies SOKENDAI | Shunan Univ | Space Telescope Sci Inst | Univ Nacl Autonoma Mexico | Univ Bonn
Abstract
We present Atacama Large Millimeter/submillimeter Array (ALMA) observations of two dense gas tracers, HCN (1−0) and HCO ^+ (1-0) for three galaxies in the green valley and two galaxies on the star-forming main sequence with comparable molecular gas fractions as traced by the CO (1−0) emissions, selected from the ALMaQUEST survey. We investigate whether the deficit of molecular gas star formation efficiency (SFE _mol ) that leads to the low specific star formation rate (sSFR) in these green valley galaxies is due to a lack of dense gas (characterized by the dense gas fraction f _dense ) or the low star formation efficiency of dense gas (SFE _dense ). We find that SFE _mol as traced by the CO emissions, when considering both star-forming and retired spaxels together, is tightly correlated with SFE _dense and depends only weakly on f _dense . The sSFR on kiloparsec scales is primarily driven by SFE _mol and SFE _dense , followed by the dependence on f _mol , and is least correlated with f _dense or the dense-gas-to-stellar mass ratio ( R _dense ). When compared with other works in the literature, we find that our green valley sample shows lower global SFE _mol and lower SFE _dense while exhibiting similar dense gas fractions when compared to star-forming and starburst galaxies. We conclude that the star formation of the three green valley galaxies with a normal abundance of molecular gas is suppressed, mainly due to the reduced SFE _dense rather than the lack of dense gas.
MoreTranslated text
Key words
Galaxy evolution,Green valley galaxies,Molecular gas
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话