论文标题
适应基于流量的重力波的噪声分布变化
Adapting to noise distribution shifts in flow-based gravitational-wave inference
论文作者
论文摘要
重力波参数估计的深度学习技术已成为标准采样器$ \ unicode {x2013} $产生可比精度的结果的快速替代方法。这些方法（例如，丁戈）通过训练归一化流量来代表贝叶斯后部条件，从而在观察到的数据中降低了推断。通过对噪声功率频谱密度（PSD）进行调节，它们甚至可以考虑变化的检测器特征。但是，培训此类网络需要事先知道预期观察到的PSD的分布，因此只有在收集了所有数据进行分析后才能进行。在这里，我们开发了一个概率模型来预测未来的PSD，从而大大增加了Dingo网络的时间范围。使用来自第二个Ligo-Virgo观察运行（O2）$ \ UNICODE {X2013} $加上仅从第三个（O3）$ \ Unicode {X2013} $的单个PSD的PSD，我们可以显示我们可以训练我们可以训练一个Dingo网络以在37个实际事件中执行准确的推理（在37个实际事件中都能进行准确的推理。因此，我们期望这种方法是一种关键组成部分，可以使深度学习技术用于重力波的低延迟分析。
Deep learning techniques for gravitational-wave parameter estimation have emerged as a fast alternative to standard samplers $\unicode{x2013}$ producing results of comparable accuracy. These approaches (e.g., DINGO) enable amortized inference by training a normalizing flow to represent the Bayesian posterior conditional on observed data. By conditioning also on the noise power spectral density (PSD) they can even account for changing detector characteristics. However, training such networks requires knowing in advance the distribution of PSDs expected to be observed, and therefore can only take place once all data to be analyzed have been gathered. Here, we develop a probabilistic model to forecast future PSDs, greatly increasing the temporal scope of DINGO networks. Using PSDs from the second LIGO-Virgo observing run (O2) $\unicode{x2013}$ plus just a single PSD from the beginning of the third (O3) $\unicode{x2013}$ we show that we can train a DINGO network to perform accurate inference throughout O3 (on 37 real events). We therefore expect this approach to be a key component to enable the use of deep learning techniques for low-latency analyses of gravitational waves.
