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 language | English |
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Title of host publication | Proceedings of ICPR 2020 - 25th International Conference on Pattern Recognition |
Pages | 9643-9649 |
Number of pages | 7 |
ISBN (Electronic) | 9781728188089 |
DOIs | |
State | Published - 2020 |
Event | 25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy Duration: Jan 10 2021 → Jan 15 2021 |
Publication series
Name | Proceedings - International Conference on Pattern Recognition |
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ISSN (Print) | 1051-4651 |
Conference
Conference | 25th International Conference on Pattern Recognition, ICPR 2020 |
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Country/Territory | Italy |
City | Virtual, Milan |
Period | 1/10/21 → 1/15/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition