As it stands today, the search for extraterrestrial intelligence (SETI) is highly dependent on our ability to detect interesting candidate signals, or technosignatures, in radio telescope observations and distinguish these from human radio frequency interference (RFI). Current signal search pipelines look for signals in spectrograms of intensity as a function of time and frequency (which can be thought of as images), but tend to do poorly in identifying multiple signals in a single data frame. This is especially apparent when there are dim signals in the same frame as bright, high signal-to-noise ratio (SNR) signals. In this work, we approach this problem using convolutional neural networks (CNN) as a computationally efficient method for localizing signals in synthetic observations resembling data collected by Breakthrough Listen using the Green Bank Telescope. We generate two synthetic datasets, the first with exactly one signal at various SNR levels and the second with exactly two signals, one of which represents RFI. We find that a residual CNN with strided convolutions and using multiple image normalizations as input outperforms a more basic CNN with max pooling trained on inputs with only one normalization. Training each model on a smaller subset of the training data at higher SNR levels results in a significant increase in model performance, reducing root mean square errors by at least a factor of 3 at an SNR of 25 dB. Although each model produces outliers with significant error, these results demonstrate that using CNNs to analyze signal location is promising, especially in image frames that are crowded with multiple signals.

中文翻译：

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噪声合成谱数据上 SETI 的窄带信号定位

就目前而言，对地外文明 (SETI) 的搜索高度依赖于我们在射电望远镜观测中检测有趣的候选信号或技术特征并将其与人类无线电频率干扰 (RFI) 区分开来的能力。当前的信号搜索管道在强度频谱图中寻找信号作为时间和频率的函数（可以被认为是图像），但在识别单个数据帧中的多个信号方面往往表现不佳。当与明亮的高信噪比 (SNR) 信号在同一帧中存在暗淡信号时，这一点尤其明显。在这项工作中，我们使用卷积神经网络 (CNN) 作为一种计算上有效的方法来解决这个问题，用于在合成观测中定位信号，类似于使用 Green Bank 望远镜通过 Breakthrough Listen 收集的数据。我们生成两个合成数据集，第一个具有不同 SNR 级别的恰好一个信号，第二个具有恰好两个信号，其中一个代表 RFI。我们发现，具有跨步卷积并使用多个图像归一化作为输入的残差 CNN 的性能优于更基本的 CNN，该 CNN 在仅具有一个归一化的输入上训练最大池化。在较高 SNR 水平下使用较小的训练数据子集训练每个模型会显着提高模型性能，在 SNR 为 25 dB 时将均方根误差降低至少 3 倍。