卷积神经网络：引力波探测的灵丹妙药？
Convolutional neural networks: a magic bullet for gravitational-wave detection?
摘要：
在过去的几年中，机器学习技术，特别是卷积神经网络，已经被研究作为一种方法来取代或补充传统的匹配滤波技术，用于检测合并黑洞的引力波签名。然而，到目前为止，这些方法尚未成功地应用于高级LIGO和Virgo引力波天文台记录的长时间数据分析。在这项工作中，我们批判性地研究了卷积神经网络作为搜索合并黑洞的工具的使用。我们确定了这种方法的优势和局限性，强调了机器学习和引力波天文学之间转换的一些常见陷阱，并讨论了跨学科的挑战。特别是，我们详细解释了为什么卷积神经网络不能单独用于声称统计上显著的引力波检测。然而，我们展示了它们如何仍然可以用于快速标记数据中潜在信号的时间，以便进行更详细的随访。我们的卷积神经网络架构以及提出的性能指标比标准的二进制分类方案更适合这项任务。我们的方法对先进的LIGO数据的详细评估表明，这种系统作为触发发生器的潜力。最后，我们通过构建对抗性示例来提醒注意，这些示例展示了我们模型中有趣的“故障模式”，其中与真实引力波信号没有明显相似之处的输入被网络以高置信度识别出来。
英文摘要：
In the last few years, machine learning techniques, in particular convolutional neural networks, have been investigated as a method to replace or complement traditional matched filtering techniques that are used to detect the gravitational-wave signature of merging black holes. However, to date, these methods have not yet been successfully applied to the analysis of long stretches of data recorded by the Advanced LIGO and Virgo gravitational-wave observatories. In this work, we critically examine the use of convolutional neural networks as a tool to search for merging black holes. We identify the strengths and limitations of this approach, highlight some common pitfalls in translating between machine learning and gravitational-wave astronomy, and discuss the interdisciplinary challenges. In particular, we explain in detail why convolutional neural networks alone cannot be used to claim a statistically significant gravitational-wave detection. However, we demonstrate how they can still be used to rapidly flag the times of potential signals in the data for a more detailed follow-up. Our convolutional neural network architecture as well as the proposed performance metrics are better suited for this task than a standard binary classifications scheme. A detailed evaluation of our approach on Advanced LIGO data demonstrates the potential of such systems as trigger generators. Finally, we sound a note of caution by constructing adversarial examples, which showcase interesting "failure modes" of our model, where inputs with no visible resemblance to real gravitational-wave signals are identified as such by the network with high confidence.
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