Generating Event Triggers Based on Hilbert-Huang Transform and Its Application to Gravitational-Wave Data

TL;DR

This paper introduces EtaGen, a new event trigger generator based on the Hilbert-Huang transform, which adaptively decomposes data to detect gravitational-wave signals more effectively than existing methods.

Contribution

The paper presents EtaGen, a novel adaptive mode decomposition method for gravitational-wave event detection, outperforming or matching current state-of-the-art algorithms like Omicron.

Findings

EtaGen successfully detects simulated gravitational-wave signals.

EtaGen's performance is comparable or superior to Omicron.

The method effectively isolates transient signals in noisy data.

Abstract

We present a new event trigger generator based on the Hilbert-Huang transform, named EtaGen ($\eta$Gen). It decomposes a 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 in the LIGO simulated data are found. 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.