论文标题
带机器学习算法的龙虾眼X射线望远镜的目标检测框架
Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine Learning Algorithms
论文作者
论文摘要
龙虾眼望远镜是检测X射线瞬变的理想监测器，因为它们可以在X射线频带中的广阔视野上观察到天体。但是，通过龙虾眼望远镜获得的图像通过其独特的点扩散功能进行了修改，因此很难设计高效率目标检测算法。在本文中，我们集成了几种机器学习算法，以构建龙虾眼望远镜获得的数据的目标检测框架。我们的框架将根据检测器上的光子位置生成两个具有不同像素尺度的2D图像。然后，将使用基于形态操作和两个神经网络的算法来检测与这些2D图像不同通量不同的天体对象的候选物。最后，将使用随机的森林算法来获取以前步骤获得的候选者的最终检测结果。经过爱因斯坦探测器上的宽视野X射线望远镜的模拟数据，我们的检测框架可以实现超过94％的纯度和超过90％的完整性，该目标的通量超过3 MCRAB（9.6 * 10-11 ERG/CM2/s），对于较低的时间效果，纯度和中度的完整性超过94％的纯度和中等程度。本文提出的框架可以用作针对其他龙虾眼X射线望远镜开发的数据处理方法的参考。
Lobster eye telescopes are ideal monitors to detect X-ray transients, because they could observe celestial objects over a wide field of view in X-ray band. However, images obtained by lobster eye telescopes are modified by their unique point spread functions, making it hard to design a high efficiency target detection algorithm. In this paper, we integrate several machine learning algorithms to build a target detection framework for data obtained by lobster eye telescopes. Our framework would firstly generate two 2D images with different pixel scales according to positions of photons on the detector. Then an algorithm based on morphological operations and two neural networks would be used to detect candidates of celestial objects with different flux from these 2D images. At last, a random forest algorithm will be used to pick up final detection results from candidates obtained by previous steps. Tested with simulated data of the Wide-field X-ray Telescope onboard the Einstein Probe, our detection framework could achieve over 94% purity and over 90% completeness for targets with flux more than 3 mCrab (9.6 * 10-11 erg/cm2/s) and more than 94% purity and moderate completeness for targets with lower flux at acceptable time cost. The framework proposed in this paper could be used as references for data processing methods developed for other lobster eye X-ray telescopes.
