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arXiv 1901.04079astro-ph.IMastro-ph.HEhep-ph
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基于深度学习的Cosmic Ray诱导空气簇射无线电信号分类和恢复

Classification and Recovery of Radio Signals from Cosmic Ray Induced Air Showers with Deep Learning

M. Erdmann, F. Schlueter, R. Smida

摘要:

来自空气簇射的无线电发射使宇宙粒子运动学和身份的测量成为可能。在连续背景噪声中,在宽带兆赫天线中检测无线电信号。我们提出了两个深度学习概念及其应用于模拟数据时的性能。第一个网络将时间轨迹分类为信号或背景。对于大于3的信噪比,我们实现了约90%的真阳性率,假阳性率低于0.2%。另一个网络用于从背景中清除时间轨迹,并恢复源于空气簇射的无线电时间轨迹。在这里,我们实现了轨迹中包含的能量的分辨率约为20%,而没有信号轨迹的$80\%$的偏差。所获得的频谱从射频干扰的信号中清除,并显示出预期的形状。

英文摘要:

Radio emission from air showers enables measurements of cosmic particle kinematics and identity. The radio signals are detected in broadband Megahertz antennas among continuous background noise. We present two deep learning concepts and their performance when applied to simulated data. The first network classifies time traces as signal or background. We achieve a true positive rate of about 90% for signal-to-noise ratios larger than three with a false positive rate below 0.2%. The other network is used to clean the time trace from background and to recover the radio time trace originating from an air shower. Here we achieve a resolution in the energy contained in the trace of about 20% without a bias for $80\%$ of the traces with a signal. The obtained frequency spectrum is cleaned from signals of radio frequency interference and shows the expected shape.

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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