利用 CNN 和 FPGA 改进双中子星引力波的早期探测
Improving early detection of gravitational waves from binary neutron stars using CNNs and FPGAs
AI总结：
本文提出基于 WaveNet 并面向 FPGA 部署的 BNS 引力波早期预警方法，在 O3 模拟数据上将 1% 虚警概率下准确率提升至 76.22%，并评估了多平台成本。
摘要：
探测来自双中子星（BNS）的引力波（GWs），并结合可能的望远镜后续观测，为多信使天文学领域的突破性发现打开了一扇窗口。随着当前和未来引力波探测器灵敏度的提高，预计未来将有更多 BNS 探测。因此，增强低延迟引力波搜索算法以实现快速、高精度和低计算成本至关重要。降低延迟的一种创新解决方案是使用嵌入现场可编程门阵列（FPGA）中的机器学习（ML）方法。在这项工作中，我们提出一种基于 WaveNet 的新方法，利用这一最先进的 ML 模型为 BNS 系统生成预警。作为概念验证数据集，我们使用嵌入 Advanced LIGO 和 Advanced Virgo 探测器第三次观测运行（O3）高斯噪声中的模拟引力波信号，展示了显著的性能提升。与当前领先的基于 ML 的预警系统相比，在 1% 虚警概率下，我们的方法将探测准确率从 66.81% 提高到 76.22%。此外，我们评估了模型在 CPU、GPU 和 FPGA 平台上的时间、能源和经济成本，展示了其部署到实时引力波探测流水线中的潜力。
英文摘要：
The detection of gravitational waves (GWs) from binary neutron stars (BNSs) with possible telescope follow-ups opens a window to ground-breaking discoveries in the field of multi-messenger astronomy. With the improved sensitivity of current and future GW detectors, more BNS detections are expected in the future. Therefore, enhancing low-latency GW search algorithms to achieve rapid speed, high accuracy, and low computational cost is essential. One innovative solution to reduce latency is the use of machine learning (ML) methods embedded in field-programmable gate arrays (FPGAs). In this work, we present a novel \texttt{WaveNet}-based method, leveraging the state-of-the-art ML model, to produce early-warning alerts for BNS systems. Using simulated GW signals embedded in Gaussian noise from the Advanced LIGO and Advanced Virgo detectors' third observing run (O3) as a proof-of-concept dataset, we demonstrate significant performance improvements. Compared to the current leading ML-based early-warning system, our approach enhances detection accuracy from 66.81\% to 76.22\% at a 1\% false alarm probability. Furthermore, we evaluate the time, energy, and economical cost of our model across CPU, GPU, and FPGA platforms, showcasing its potential for deployment in real-time gravitational wave detection pipelines.
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