利用液态状态机推进射电天文中的射频干扰检测
Advancing RFI-Detection in Radio Astronomy with Liquid State Machines
AI总结：
本文利用液态状态机改进射电天文中的射频干扰检测，采用二阶泄漏积分和放电神经元和Transformer解码器，实现98%像素精度和0.743 F1分数。
摘要：
射频干扰（RFI）来自人为无线电源对当前和未来的射电望远镜构成了重大挑战。当前检测RFI的方法通常将任务视为对射电望远镜频谱图的语义分割问题。通常，复杂的启发式算法结合手动标注（在最困难的情况下）来处理这一'标记'任务。尽管最近的机器学习方法已显示出高精度，但它们往往无法满足现代射电天文台的严格操作要求。由于其本质上的时间变化性，脉冲神经网络（SNNs）成为RFI检测的有希望的替代方法，通过利用频谱图源数据的时间变化性。在本工作中，我们应用液态状态机（LSMs），一种脉冲神经网络，用于RFI检测。我们采用了二阶泄漏积分和放电（LiF）神经元，这标志着该架构和神经元类型首次用于RFI检测。我们测试了三种编码方法和三种越来越复杂的读取层，包括一个Transformer解码器头，提供了一种SNN和ANN技术的混合方法。我们的方法将LSMs扩展到传统的分类任务之外，用于精细的时空分割。我们训练LSMs在由氢的再电离时期阵列（HERA）衍生的模拟数据上，HERA是已知的RFI检测基准。我们的模型在每个像素上的精度达到98%，F1分数为0.743，显示出在这一极具挑战性的任务上的竞争性表现。这项工作扩展了应用于RFI检测的SNN技术及其架构的复杂性，并突显了LSMs在处理精细的、复杂的、时空信号处理任务中的有效性。
英文摘要：
Radio Frequency Interference (RFI) from anthropogenic radio sources poses significant challenges to current and future radio telescopes. Contemporary approaches to detecting RFI treat the task as a semantic segmentation problem on radio telescope spectrograms. Typically, complex heuristic algorithms handle this task of `flagging' in combination with manual labeling (in the most difficult cases). While recent machine-learning approaches have demonstrated high accuracy, they often fail to meet the stringent operational requirements of modern radio observatories. Owing to their inherently time-varying nature, spiking neural networks (SNNs) are a promising alternative method to RFI-detection by utilizing the time-varying nature of the spectrographic source data. In this work, we apply Liquid State Machines (LSMs), a class of spiking neural networks, to RFI-detection. We employ second-order Leaky Integrate-and-Fire (LiF) neurons, marking the first use of this architecture and neuron type for RFI-detection. We test three encoding methods and three increasingly complex readout layers, including a transformer decoder head, providing a hybrid of SNN and ANN techniques. Our methods extend LSMs beyond conventional classification tasks to fine-grained spatio-temporal segmentation. We train LSMs on simulated data derived from the Hyrogen Epoch of Reionization Array (HERA), a known benchmark for RFI-detection. Our model achieves a per-pixel accuracy of 98% and an F1-score of 0.743, demonstrating competitive performance on this highly challenging task. This work expands the sophistication of SNN techniques and architectures applied to RFI-detection, and highlights the effectiveness of LSMs in handling fine-grained, complex, spatio-temporal signal-processing tasks.
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