Chrome Extension
WeChat Mini Program
Use on ChatGLM

WALLABY Pilot Survey: Hydra Cluster Galaxies UV and H Imorphometrics

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY(2023)

Univ Louisville | Univ Bonn | Aix Marseille Univ | Univ Lyon | Queens Univ | Netherlands Inst Radio Astron | Univ Western Australia | ARC Ctr Excellence Astrophys 3 Dimens ASTRO 3D | Univ Calgary | Sejong Univ | Royal Mil Coll Canada | Peking Univ

Cited 2|Views16
Abstract
Galaxy morphology in atomic hydrogen (H i) and in the ultraviolet (UV) are closely linked. This has motivated their combined use to quantify morphology over the full H i disc for both H i and UV imaging. We apply galaxy morphometrics: concentration, asymmetry, gini, M-20 and multimode-intensity-deviation statistics to the first moment-0 maps of the WALLABY Survey of galaxies in the hydra cluster centre. Taking advantage of this new H i survey, we apply the same morphometrics over the full H i extent on archival GALEX FUV and NUV data to explore how well H i truncated, extended ultraviolet disc (XUV) and other morphological phenomena can be captured using pipeline WALLABY data products. Extended H i and UV discs can be identified relatively straightforward from their respective concentration. Combined with WALLABY H i, even the shallowest GALEX data are sufficient to identify XUV discs. Our second goal is to isolate galaxies undergoing ram-pressure stripping in the H i morphometric space. We employ four different machine learning techniques, a decision tree, a k-nearest neighbour, a support-vector machine, and a random forest. Up to 80 per cent precision and recall are possible with the random forest giving the most robust results.
More
Translated text
Key words
galaxies: disc,galaxies: ISM,galaxies: kinematics and dynamics,galaxies: spiral,galaxies: statistics,galaxies: structure
PDF
Bibtex
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
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

要点】:本文利用WALLABY H i巡天数据和GALEX紫外数据,通过形态计量学方法研究银河系在原子氢和紫外波段形态的关联性,并运用机器学习技术识别受到冲压剥离影响的星系。

方法】:文章采用形态计量学指标(如浓度、不对称性、Gini系数、M-20和多重模式强度偏差统计)对WALLABY巡天的第一时刻0图进行量化分析,并将同样的指标应用于存档的GALEX FUV和NUV数据。

实验】:研究使用WALLABY巡天数据对Hydra星团中心的星系进行形态计量分析,并在存档的GALEX FUV和NUV数据上应用相同方法,实验结果表明,通过WALLABY H i数据和GALEX紫外数据可以有效地识别扩展的H i和紫外盘。此外,采用四种机器学习技术(决策树、k-最近邻、支持向量机和随机森林)对受冲压剥离影响的星系进行识别,随机森林技术给出了最稳健的结果,达到80%的精确度和召回率。