Multi-branch attention networks for classifying galaxy clusters

Yu Zhang, Gongbo Liang, Yuanyuan Su, Nathan Jacobs

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

This paper addresses the task of classifying galaxy clusters, which are the largest known objects in the Universe. Galaxy clusters can be categorized as cool-core (CC), weak-cool-core (WCC), and non-cool-core (NCC), depending on their central cooling times. Traditional classification approaches used in astrophysics are inaccurate and rely on measuring surface brightness concentrations or central gas densities. In this work, we propose a multi-branch attention network that uses spatial attention to classify a given cluster. To evaluate our network, we use a database of simulated X-ray emissivity images, which contains 954 projections of 318 clusters. Experimental results show that our network outperforms several strong baseline methods and achieves a macro-averaged F1 score of 0.83. We highlight the value of our proposed spatial attention module through an ablation study.

Original languageEnglish
Title of host publicationProceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
Pages9643-9649
Number of pages7
ISBN (Electronic)9781728188089
DOIs
StatePublished - 2020
Event25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy
Duration: Jan 10 2021Jan 15 2021

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Conference25th International Conference on Pattern Recognition, ICPR 2020
Country/TerritoryItaly
CityVirtual, Milan
Period1/10/211/15/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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