论文标题
使用条件变分的自动编码器来分析环阵重力波
Use of conditional variational auto encoder to analyze ringdown gravitational waves
论文作者
论文摘要
最近，提出了几种深度学习方法进行重力波数据分析。一种是有条件的变异自动编码器（CVAE），该编码器由Gabbard等人提出。 [1]。我们研究了CVAE的准确性，在估计环的QNM频率的背景下。我们表明，CVAE估计的准确性优于匹配的过滤。还比较了置信区域，并表明CVAE可以返回较小的置信区域。此外，我们评估了CVAE估计的置信区的可靠性。我们的工作证实，深度学习方法具有与匹配的过滤竞争或克服竞争的能力。
Recently, several deep learning methods are proposed for the gravitational wave data analysis. One is conditional variational auto encoder (CVAE), proposed by Gabbard et al. [1]. We study the accuracy of a CVAE in the context of the estimation of the QNM frequency of the ringdown. We show that the accuracy of the estimation by the CVAE is better than the matched filtering. The areas of confidence regions are also compared and it is shown that the CVAE can return smaller confidence regions. Also, we assess the reliability of the confidence regions estimated by the CVAE. Our work confirms that the deep learning method has ability to compete with or overcome the matched filtering.
