# X-ray Astronomical Point Sources Recognition Using Granular Binary-tree SVM

## TL;DR

This paper introduces a novel granular binary-tree SVM classifier for identifying point sources in X-ray astronomical images, achieving higher accuracy than existing methods.

## Contribution

The paper presents a new GBT-SVM classifier specifically designed for recognizing celestial point sources in X-ray images, improving detection accuracy.

## Key findings

- Achieves around 89% accuracy in point source recognition.
- Outperforms other SVM-based classifiers in precision and recall.
- Validated on real astronomical images.

## Abstract

The study on point sources in astronomical images is of special importance, since most energetic celestial objects in the Universe exhibit a point-like appearance. An approach to recognize the point sources (PS) in the X-ray astronomical images using our newly designed granular binary-tree support vector machine (GBT-SVM) classifier is proposed. First, all potential point sources are located by peak detection on the image. The image and spectral features of these potential point sources are then extracted. Finally, a classifier to recognize the true point sources is build through the extracted features. Experiments and applications of our approach on real X-ray astronomical images are demonstrated. comparisons between our approach and other SVM-based classifiers are also carried out by evaluating the precision and recall rates, which prove that our approach is better and achieves a higher accuracy of around 89%.