Discovery of peculiar radio morphologies with ASKAP using unsupervised machine learning

被引:5
|
作者
Gupta, Nikhel [1 ]
Minh Huynh [1 ,2 ]
Norris, Ray P. [3 ,4 ]
Wang, X. Rosalind [3 ]
Hopkins, Andrew M. [3 ,5 ]
Andernach, Heinz [6 ]
Koribalski, Barbel S. [3 ,4 ]
Galvin, Tim J. [7 ]
机构
[1] CSIRO, Space & Astron, POB 1130, Bentley, WA 6102, Australia
[2] Univ Western Australia, Int Ctr Radio Astron Res ICRAR, M468,35 Stirling Highway, Crawley, WA 6009, Australia
[3] Western Sydney Univ, Locked Bag 1797, Penrith, NSW 2751, Australia
[4] CSIRO, Space & Astron, POB 76, Epping, NSW 1710, Australia
[5] Macquarie Univ, Australian Astron Opt, 105 Delhi Rd, N Ryde, NSW 2113, Australia
[6] Univ Guanajuato, DCNE, Dept Astron, Cjon Jalisco S-N, Guanajuato 36023, Mexico
[7] Curtin Univ, Int Ctr Radio Astron Res, Bentley, WA 6102, Australia
关键词
galaxies; active; peculiar; radio continuum; Galaxy; evolution; methods; data analysis; STAR-FORMATION RATE; GALAXY CLUSTERS; SOURCE CATALOG; WIDE-FIELD; SKY; CLASSIFICATION; LUMINOSITY; EMISSION; COMPACT; SCIENCE;
D O I
10.1017/pasa.2022.44
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
We present a set of peculiar radio sources detected using an unsupervised machine learning method. We use data from the Australian Square Kilometre Array Pathfinder (ASKAP) telescope to train a self-organizing map (SOM). The radio maps from three ASKAP surveys, Evolutionary Map of Universe pilot survey (EMU-PS), Deep Investigation of Neutral Gas Origins pilot survey (DINGO), and Survey With ASKAP of GAMA-09 + X-ray (SWAG-X), are used to search for the rarest or unknown radio morphologies. We use an extension of the SOM algorithm that implements rotation and flipping invariance on astronomical sources. The SOM is trained using the images of all 'complex' radio sources in the EMU-PS which we define as all sources catalogued as 'multi-component'. The trained SOM is then used to estimate a similarity score for complex sources in all surveys. We select 0.5% of the sources that are most complex according to the similarity metric and visually examine them to find the rarest radio morphologies. Among these, we find two new odd radio circle (ORC) candidates and five other peculiar morphologies. We discuss multiwavelength properties and the optical/infrared counterparts of selected peculiar sources. In addition, we present examples of conventional radio morphologies including: diffuse emission from galaxy clusters, and resolved, bent-tailed, and FR-I and FR-II type radio galaxies. We discuss the overdense environment that may be the reason behind the circular shape of ORC candidates.
引用
收藏
页数:21
相关论文
共 50 条
  • [1] Exploration of tissue morphologies in breast cancer samples using unsupervised machine learning
    Turkki, Riku
    Bychkov, Dmitrii
    Linder, Nina
    Isola, Jorma
    Joensuu, Heikki
    Lundin, Johan
    [J]. CANCER RESEARCH, 2017, 77
  • [2] Identifying Uncertain Galaxy Morphologies Using Unsupervised Learning
    Edwards, Kieran Jay
    Gaber, Mohamed Medhat
    [J]. ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, PT II, 2013, 7895 : 146 - 157
  • [3] Unsupervised machine learning accelerates solid electrolyte discovery
    Zhang, Xu
    Tang, Bin
    Zhou, Zhen
    [J]. GREEN ENERGY & ENVIRONMENT, 2021, 6 (01) : 3 - 4
  • [4] Unsupervised machine learning accelerates solid electrolyte discovery
    Xu Zhang
    Bin Tang
    Zhen Zhou
    [J]. Green Energy & Environment, 2021, 6 (01) : 3 - 4
  • [5] Discovery of distinct cancer cachexia phenotypes using an unsupervised machine-learning algorithm
    Wu, Hao-Fan
    Yan, Jiang-Peng
    Wu, Qian
    Yu, Zhen
    Xu, Hong-Xia
    Song, Chun-Hua
    Guo, Zeng-Qing
    Li, Wei
    Xiang, Yan-Jun
    Xu, Zhe
    Luo, Jie
    Cheng, Shu-Qun
    Zhang, Feng-Min
    Shi, Han-Ping
    Zhuang, Cheng-Le
    [J]. NUTRITION, 2024, 119
  • [6] Postcardiac Arrest Physiological Endotype Discovery With Unsupervised Machine Learning
    Kim, Han B.
    Afshar, Ali Sobhi
    Stevens, Robert D.
    [J]. CIRCULATION, 2020, 142 (24) : E499 - E500
  • [7] Clustering superconductors using unsupervised machine learning
    Roter, B.
    Ninkovic, N.
    Dordevic, S. V.
    [J]. PHYSICA C-SUPERCONDUCTIVITY AND ITS APPLICATIONS, 2022, 598
  • [8] Distributed unsupervised learning using the multisoft machine
    Patané, G
    Russo, M
    [J]. INFORMATION SCIENCES, 2002, 143 (1-4) : 181 - 196
  • [9] Bot detection using unsupervised machine learning
    Wei Wu
    Jaime Alvarez
    Chengcheng Liu
    Hung-Min Sun
    [J]. Microsystem Technologies, 2018, 24 : 209 - 217
  • [10] Prospectivity analysis using unsupervised machine learning
    Aranha, Malcolm
    Porwal, Alok
    [J]. 16TH SGA BIENNIAL MEETING, 2022, VOL 1, 2022, : 9 - 12