论文标题
在重力波检测中的常见空间模式的应用
Application of Common Spatial Patterns in Gravitational Waves Detection
论文作者
论文摘要
常见的空间模式（CSP）是一种特征提取算法，广泛用于大脑计算机界面（BCI）系统，用于在多通道磁磁/脑电图（MEG/EEG）时间序列数据中检测与事件相关电位（ERP）。在本文中，我们将CSP算法开发并应用于确定给定的多探测器引力波（GW）菌株是否包含融合的问题。与信号处理技术和逻辑回归分类器配对，我们发现我们的管道使用H1和L1菌株可以正确地检测到重力波瞬时目录的82个自信事件中的76个，分类得分为$ 93.72 \ pm pm 0.04 \％\％\％\％$ $ 10 \％，使用$ 10 \ tims $ tims $ times $ times $ $ $交叉验证。假阴性事件是：GW170817-V3，GW191219 163120-V1，GW200115 042309-V2，GW200210 092254-V1，GW200220 061928-V1，和GW2003222222222091133-V1。
Common Spatial Patterns (CSP) is a feature extraction algorithm widely used in Brain-Computer Interface (BCI) Systems for detecting Event-Related Potentials (ERPs) in multi-channel magneto/electroencephalography (MEG/EEG) time series data. In this article, we develop and apply a CSP algorithm to the problem of identifying whether a given epoch of multi-detector Gravitational Wave (GW) strains contains coalescenses. Paired with Signal Processing techniques and a Logistic Regression classifier, we find that our pipeline is correctly able to detect 76 out of 82 confident events from Gravitational Wave Transient Catalog, using H1 and L1 strains, with a classification score of $93.72 \pm 0.04\%$ using $10 \times 5$ cross validation. The false negative events were: GW170817-v3, GW191219 163120-v1, GW200115 042309-v2, GW200210 092254-v1, GW200220 061928-v1, and GW200322 091133-v1.
