arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.11066astro-ph.IM
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应用显著图分析搜索射电暴和快速射电暴

Applying saliency-map analysis in searches for pulsars and fast radio bursts

C. Zhang, C. Wang, G. Hobbs, C. J. Russell, D. Li, S. -B. Zhang, S. Dai, J. -W. Wu, Z. -C. Pan, W. -W. Zhu, L. Toomey, Z. -Y. Ren

摘要:

研究使用显着图分析来帮助搜索瞬态信号,例如快速射电暴和来自射电望远镜的单个脉冲。我们的目标是证明显着图提供了理解机器学习算法预测的方法,并且可以在用于搜索瞬态事件的流水线中实现。我们已经实现了一种新的深度学习方法来预测数据的任何部分是否包含瞬态事件。该算法已经使用真实和模拟数据集进行了训练。我们证明,该算法是能够识别这样的事件。通过使用显着图对输出结果进行视觉分析。我们发现,显着图可以产生任何瞬态功能的增强图像,而不需要去色散或去除射频干扰。这样的地图可以用来理解图像中的哪些特征用于做出机器学习决策,并可视化瞬态事件。尽管这里报告的算法是为了证明显着图分析,我们已经检测到,在档案数据中,一个单一的突发事件与分散测量$41$ \,厘米$^{-3}$ pc,这是不相关的任何目前已知的脉冲星。

英文摘要:

To investigate the use of saliency-map analysis to aid in searches for transient signals, such as fast radio bursts and individual pulses from radio pulsars. We aim to demonstrate that saliency maps provide the means to understand predictions from machine learning algorithms and can be implemented in piplines used to search for transient events. We have implemented a new deep learning methodology to predict whether or not any segment of the data contains a transient event. The algorithm has been trained using real and simulated data sets. We demonstrate that the algorithm is able to identify such events. The output results are visually analysed via the use of saliency maps. We find that saliency maps can produce an enhanced image of any transient feature without the need for de-dispersion or removal of radio frequency interference. Such maps can be used to understand which features in the image were used in making the machine learning decision and to visualise the transient event. Even though the algorithm reported here was developed to demonstrate saliency-map analysis, we have detected, in archival data, a single burst event with dispersion measure of $41$\,cm$^{-3}$pc that is not associated with any currently known pulsar.

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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