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arXiv 2203.16607astro-ph.IM
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射电天文中RFI检测和消除的自学习神经网络方法

A Self-Learning Neural Network Approach for RFI Detection and Removal in Radio Astronomy

Benjamin R. B. Saliwanchik, Anže Slosar

摘要:

提出了一种新的神经网络(NN)的方法来检测和消除射频干扰(RFI)的原始数字化信号中的信号处理链的一个典型的射电天文实验。我们的方法的主要优点是它不需要训练集。相反,我们的方法依赖于这样一个事实,即来自天文源的真正感兴趣的信号是热的,因此被描述为高斯随机过程,不能被压缩。我们采用变分编码器/解码器网络,以找到数据流中的可压缩信息,可以解释最少的自由度的变化。我们演示了一组玩具的问题,并存储从重子映射实验(BMX)原型的环形缓冲区。我们发现RFI减法在清洁模拟时间流方面是有效的:虽然我们发现NN输出的RFI清洁时间流的功率谱受到与加性噪声一致的额外信号的影响,但我们发现即使RFI功率比信号大一个数量级,它通常在整个频带的百分比水平和污染频谱通道中的10%以下。我们讨论了这种方法的优点和局限性,并可能在未来的无线电实验的前端实现。

英文摘要:

We present a novel neural network (NN) method for the detection and removal of Radio Frequency Interference (RFI) from the raw digitized signal in the signal processing chain of a typical radio astronomy experiment. The main advantage of our method is that it does not require a training set. Instead, our method relies on the fact that the true signal of interest coming from astronomical sources is thermal and therefore described as a Gaussian random process, which cannot be compressed. We employ a variational encoder/decoder network to find the compressible information in the datastream that can explain the most variance with the fewest degrees of freedom. We demonstrate it on a set of toy problems and stored ringbuffers from the Baryon Mapping eXperiment (BMX) prototype. We find that the RFI subtraction is effective at cleaning simulated timestreams: while we find that the power spectra of the RFI-cleaned timestreams output by the NN suffer from extra signal consistent with additive noise, we find that it is generally around percent level across the band and sub 10 percent in contaminated spectral channels even when RFI power is an order of magnitude larger than the signal. We discuss advantages and limitations of this method and possible implementation in the front-end of future radio experiments.

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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