We present a new event trigger generator based on the Hilbert–Huang transform, named EtaGen (\(\eta\)Gen). It decomposes time-series data into several adaptive modes without imposing a priori bases on the data. The adaptive modes are used to find transients (excesses) in the background noises. A clustering algorithm is used to gather excesses corresponding to a single event and to reconstruct its waveform. The performance of EtaGen is evaluated by how many injections are found in the LIGO simulated data. EtaGen is viable as an event trigger generator when compared directly with the performance of Omicron, which is currently the best event trigger generator used in the LIGO Scientific Collaboration and Virgo Collaboration.

我们介绍了一个基于Hilbert-Huang变换的新事件触发生成器，名为EtaGen（\（\ eta \） Gen）。它将时间序列数据分解为几种自适应模式，而无需在数据上施加先验基础。自适应模式用于查找背景噪声中的瞬态（过剩）。聚类算法用于收集与单个事件相对应的超量并重建其波形。通过在LIGO模拟数据中发现多少次进样来评估EtaGen的性能。与Omicron的性能直接比较时，EtaGen可作为事件触发生成器，而Omicron是目前在LIGO Scientific Collaboration和处女座协作中使用的最佳事件触发生成器。