Centrally Concentrated Molecular Gas Driving Galactic-Scale Ionized Gas Outflows in Star-Forming Galaxies
Monthly Notices of the Royal Astronomical Society(2021)SCI 2区
UCL | Univ Western Australia | Cardiff Univ | ARC Ctr Excellence All Sky Astrophys 3 Dimens AST | Macquarie Univ | NASA | Swinburne Univ Technol
Abstract
ABSTRACT We perform a joint analysis of high spatial resolution molecular gas and star-formation rate (SFR) maps in main-sequence star-forming galaxies experiencing galactic-scale outflows of ionized gas. Our aim is to understand the mechanism that determines which galaxies are able to launch these intense winds. We observed CO(1→0) at 1-arcsec resolution with ALMA in 16 edge-on galaxies, which also have 2-arcsec spatial-resolution optical integral field observations from the SAMI Galaxy Survey. Half the galaxies in the sample were previously identified as harbouring intense and large-scale outflows of ionized gas (‘outflow types’) and the rest serve as control galaxies. The data set is complemented by integrated CO(1→0) observations from the IRAM 30-m telescope to probe the total molecular gas reservoirs. We find that the galaxies powering outflows do not possess significantly different global gas fractions or star-formation efficiencies when compared with a control sample. However, the ALMA maps reveal that the molecular gas in the outflow-type galaxies is distributed more centrally than in the control galaxies. For our outflow-type objects, molecular gas and star-formation are largely confined within their inner effective radius (reff), whereas in the control sample, the distribution is more diffuse, extending far beyond reff. We infer that outflows in normal star-forming galaxies may be caused by dynamical mechanisms that drive molecular gas into their central regions, which can result in locally enhanced gas surface density and star-formation.
MoreTranslated text
Key words
galaxies: kinematics and dynamics,galaxies: star formation,submillimetre: galaxies,galaxies: starburst,galaxies: ISM,galaxies: evolution
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
SDSS-IV MaNGA: Enhanced Star Formation in Galactic-Scale Outflows
Monthly Notices of the Royal Astronomical Society 2021
被引用4
Frequency and Nature of Central Molecular Outflows in Nearby Star-Forming Disk Galaxies
Astronomy and Astrophysics 2021
被引用24
Kinematics of Molecular Gas in Star-Forming Galaxies with Large-Scale Ionized Outflows
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 2023
被引用1
Galaxy Pairs in the Sloan Digital Sky Survey – XV. Properties of Ionized Outflows
Monthly Notices of the Royal Astronomical Society 2022
被引用4
3D Modeling of the Molecular Gas Kinematics in Optically Selected Jellyfish Galaxies
ASTROPHYSICAL JOURNAL 2023
被引用3
Astronomy and Astrophysics 2023
被引用10
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