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arXiv 2209.04430astro-ph.IMcs.AIcs.LG
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SKA脉冲星搜索管线中机器学习方法的研究

Investigation of a Machine learning methodology for the SKA pulsar search pipeline

Raman Research Institute(拉曼研究所);IBM India Private Ltd(IBM印度私人有限公司);University of Manchester(曼彻斯特大学);BITS Goa(比尔拉科学技术学院果阿分校);PES University(PES大学)

Shashank Sanjay Bhat, Thiagaraj Prabu, Ben Stappers, Atul Ghalame, Snehanshu Saha, T. S. B Sudarshan, Zafiirah Hosenie

AI总结:

本研究测试了Mask R-CNN算法用于SKA脉冲星搜索管线中的候选信号检测,通过自定义注释工具训练模型,并在模拟数据集上成功验证,展示了其未来应用前景。

摘要:

SKA脉冲星搜索管线将用于脉冲星的实时检测。像SKA这样的现代射电望远镜在其全面运行规模下将产生PB级的数据。因此,基于经验和数据驱动的算法对于候选体检测等应用变得不可或缺。在此,我们描述了测试一种最先进的目标检测算法Mask R-CNN以在SKA脉冲星搜索管线中检测候选信号的结果。我们训练了Mask R-CNN模型来检测候选图像。开发了一个自定义注释工具,以高效地标记大数据集中的感兴趣区域。我们通过在模拟数据集上检测候选信号成功演示了该算法。本文介绍了这项工作的细节,并着重强调了未来的前景。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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