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RADIO GALAXY CLASSIFICATION
Classifying Radio Galaxies using Conventional Computer Vision and Machine Learning
Burger Becker(Masters Student)
Dr Trienko Grobler(Supervisor)
Computer Science Department
WHo ARE WE?
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● Radio Galaxies have low visible light emission
● Require radio telescopes to be observed, such as the SKA
What is a Radio Galaxy?
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Fanaroff-Riley Classification
Red is “hot”
FRI on the left, hotspots near the centre
FRII on the right, hotspots near the edges
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Fanaroff-Riley Ratio
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● R = B/A
● R < 0.5 is FRI
● R > 0.5 is FRII
Why is this problem relevant?● MeerKlass survey is expected to find upwards of 200 000 radio
sources
● 300 PB of data generated from the SKA per year (once it’s functional)
● Higher resolution than previous surveys on a larger data set, better morphological features identifiable
● ASKAP is expected to find around 70 million radio sources
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Research Process
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Preprocessing of Images
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Preprocessing of images
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Separating Background with k-means
11Clustering before preprocessing Clustering after preprocessing
Automatic Ratio Extraction
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● Did not generalise well on all the data
● Harder problem than expected due to hotspots having large variability in position and brightness
● Not all sources have central hotspots
Why doesn’t it work?
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Why doesn’t it work?
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Why doesn’t it work?
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Manual Ratio Calculation
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Calculated the ratio of a 148 sources (70 FRI and 78 FRII)
Then performed automatic feature extraction on these sources
Lobe area, brightest hotspot and number of lobes were extracted automatically
Features
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Results of ClassifierRandom Forest consistently gives the best results
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Results of Classifier
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Results of Classifier
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Using all 4 features Using 3 featuresVery small sample
Is the FR-ratio still relevant?
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Ratio at 0.311 Ratio at 0.31Yes!
We can make no assumptions regarding what the new ratio would be: 1. sample is too small 2. method is not standardized
Garbage in Garbage Out
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● Set up a classifier on the same data as used by Wathela et al. (2018)
● Has an additional classes for Bent tails and Compact sources that “pollute” our original data
Wathela et al. (2018) Our Results on Random Forest
Results of Classifier
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Mean accuracy of 83 (± 1.2)%
Area importance 0.43, Hotspot Brightness 0.52, Lobe area
0.05
Results of Classifier● Removing Number of Lobes as a feature still has a high
accuracy
● Accuracy of 81.7 (±1)%
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Conclusion● Conventional techniques are a feasible alternative to
Deep Learning, especially in terms of set up time.
● The FR-ratio can potentially be used as a feature
● Be wary of blindly trusting Deep Learning methods without properly examining the feature space
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Question to the Audience
Better method of automatic Ratio Extraction?
Preprocessing of images
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The Curve Ball
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● Bent tail radio galaxies do not have the linear structure of FRI/FRII type galaxies.
● This leads us to believe the underlying physical mechanism of the central galactic engine is different from that of the other two classes
● Visually distinct class
Results: Accuracy
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Results: What does it get wrong?
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Credits
