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Morphological Classification of Extragalactic Radio Sources Using Gradient Boosting Methods
Instrumentation and Methods for Astrophysics · Submitted 25 Apr 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim All three proposed gradient boosting methods outperformed a state-of-the-art convolutional neural networks-based classifier using less than a quarter of the number of images, with CatBoost having the highest accuracy.
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The abstract, sentence by sentence
The field of radio astronomy is witnessing a boom in the amount of data produced per day due to newly commissioned radio telescopes. One of the most crucial problems in this field is the automatic classification of extragalactic radio sources based on their morphologies. Most recent contributions in the field of morphological classification of extragalactic radio sources have proposed classifiers based on convolutional neural networks. Alternatively, this work proposes gradient boosting machine learning methods accompanied by principal component analysis as data-efficient alternatives to convolutional neural networks. Recent findings have shown the efficacy of gradient boosting methods in outperforming deep learning methods for classification problems with tabular data. The gradient boosting methods considered in this work are based on the XGBoost, LightGBM, and CatBoost implementations. This work also studies the effect of dataset size on classifier performance. A three-class classification problem is considered in this work based on the three main Fanaroff-Riley classes: class 0, class I, and class II, using radio sources from the Best-Heckman sample. All three proposed gradient boosting methods outperformed a state-of-the-art convolutional neural networks-based classifier using less than a quarter of the number of images, with CatBoost having the highest accuracy. This was mainly due to the superior accuracy of gradient boosting methods in classifying Fanaroff-Riley class II sources, with 3$\unicode{x2013}$4% higher recall.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The field of radio astronomy is witnessing a boom in the amount of data produced per day due to newly commissioned radio telescopes. | background 1.00 | 0.05 | 0.01 | asserted 1.00 |
| s1 | One of the most crucial problems in this field is the automatic classification of extragalactic radio sources based on their morphologies. | background 0.98 | 0.04 | 0.02 | asserted 1.00 |
| s2 | Most recent contributions in the field of morphological classification of extragalactic radio sources have proposed classifiers based on convolutional neural networks. | background 0.99 | 0.03 | 0.02 | asserted 1.00 |
| s3 | Alternatively, this work proposes gradient boosting machine learning methods accompanied by principal component analysis as data-efficient alternatives to convolutional neural networks. | contribution 0.92 | 0.69 | 0.08 | asserted 0.86 |
| s4 | Recent findings have shown the efficacy of gradient boosting methods in outperforming deep learning methods for classification problems with tabular data. | background 1.00 | 0.05 | 0.03 | asserted 1.00 |
| s5 | The gradient boosting methods considered in this work are based on the XGBoost, LightGBM, and CatBoost implementations. | method 0.99 | 0.55 | 0.02 | asserted 1.00 |
| s6 | This work also studies the effect of dataset size on classifier performance. | contribution 0.96 | 0.87 | 0.02 | asserted 0.71 |
| s7 | A three-class classification problem is considered in this work based on the three main Fanaroff-Riley classes: class 0, class I, and class II, using radio sources from the Best-Heckman sample. | method 0.89 | 0.85 | 0.03 | asserted 0.99 |
| s8 | All three proposed gradient boosting methods outperformed a state-of-the-art convolutional neural networks-based classifier using less than a quarter of the number of images, with CatBoost having the highest accuracy. | result 1.00 | 0.95 | 0.83 | asserted 1.00 |
| s9 | This was mainly due to the superior accuracy of gradient boosting methods in classifying Fanaroff-Riley class II sources, with 3$\unicode{x2013}$4% higher recall. | interpretation 0.89 | 0.87 | 0.27 | asserted 0.98 |
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The paper
Original abstract
The field of radio astronomy is witnessing a boom in the amount of data produced per day due to newly commissioned radio telescopes. One of the most crucial problems in this field is the automatic classification of extragalactic radio sources based on their morphologies. Most recent contributions in the field of morphological classification of extragalactic radio sources have proposed classifiers based on convolutional neural networks. Alternatively, this work proposes gradient boosting machine learning methods accompanied by principal component analysis as data-efficient alternatives to convolutional neural networks. Recent findings have shown the efficacy of gradient boosting methods in outperforming deep learning methods for classification problems with tabular data. The gradient boosting methods considered in this work are based on the XGBoost, LightGBM, and CatBoost implementations. This work also studies the effect of dataset size on classifier performance. A three-class classification problem is considered in this work based on the three main Fanaroff-Riley classes: class 0, class I, and class II, using radio sources from the Best-Heckman sample. All three proposed gradient boosting methods outperformed a state-of-the-art convolutional neural networks-based classifier using less than a quarter of the number of images, with CatBoost having the highest accuracy. This was mainly due to the superior accuracy of gradient boosting methods in classifying Fanaroff-Riley class II sources, with 3$\unicode{x2013}$4% higher recall.
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2 arXiv papers cite this in 4 sentences
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Cites it as background 1 paper
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Conventional machine learning has also been explored for the morphological classification of Fanaroff-Riley (FR) radio galaxies [ref] .
Classification of Radio Galaxies with trainable COSFIRE filters · Related Works · 0.96
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