论文标题
调查SKA PULSAR搜索管道的机器学习方法
Investigation of a Machine learning methodology for the SKA pulsar search pipeline
论文作者
论文摘要
SKA脉冲星搜索管道将用于实时检测脉冲星。现代射电望远镜（例如SKA）将以全面的操作生成数据。因此，基于经验和数据驱动的算法对于诸如候选检测等应用是必不可少的。在这里，我们描述了我们的发现，从测试最先进的对象检测算法称为Mask R-CNN来检测SKA PULSAR搜索管道中的候选标志。我们已经训练了蒙版R-CNN模型来检测候选图像。开发了一种自定义注释工具，以有效地标记大型数据集中感兴趣的区域。我们通过检测模拟数据集上的候选标志来成功证明了该算法。本文介绍了这项工作的详细信息，并重点介绍了未来的前景。
The SKA pulsar search pipeline will be used for real time detection of pulsars. Modern radio telescopes such as SKA will be generating petabytes of data in their full scale of operation. Hence experience-based and data-driven algorithms become indispensable for applications such as candidate detection. Here we describe our findings from testing a state of the art object detection algorithm called Mask R-CNN to detect candidate signatures in the SKA pulsar search pipeline. We have trained the Mask R-CNN model to detect candidate images. A custom annotation tool was developed to mark the regions of interest in large datasets efficiently. We have successfully demonstrated this algorithm by detecting candidate signatures on a simulation dataset. The paper presents details of this work with a highlight on the future prospects.
