Ninja data analysis with a detection pipeline based on the Hilbert-Huang Transform

TL;DR

This paper presents a novel data analysis pipeline using the Hilbert-Huang Transform to detect and characterize binary black hole signals in simulated gravitational wave data, achieving high detection accuracy and precise timing estimates.

Contribution

The study introduces a Hilbert-Huang Transform-based pipeline for gravitational wave data analysis, improving detection and characterization of binary black hole signals in noisy data.

Findings

Detected 77 out of 126 signals above SNR 5 in coincidence

Achieved high time and frequency resolution in waveform characterization

Estimated inter-detector time lag with sub-millisecond accuracy

Abstract

The Ninja data analysis challenge allowed the study of the sensitivity of data analysis pipelines to binary black hole numerical relativity waveforms in simulated Gaussian noise at the design level of the LIGO observatory and the VIRGO observatory. We analyzed NINJA data with a pipeline based on the Hilbert Huang Transform, utilizing a detection stage and a characterization stage: detection is performed by triggering on excess instantaneous power, characterization is performed by displaying the kernel density enhanced (KD) time-frequency trace of the signal. Using the simulated data based on the two LIGO detectors, we were able to detect 77 signals out of 126 above SNR 5 in coincidence, with 43 missed events characterized by signal to noise ratio SNR less than 10. Characterization of the detected signals revealed the merger part of the waveform in high time and frequency resolution, free from time-frequency uncertainty. We estimated the timelag of the signals between the detectors based on the optimal overlap of the individual KD time-frequency maps, yielding estimates accurate within a fraction of a millisecond for half of the events. A coherent addition of the data sets according to the estimated timelag eventually was used in a characterization of the event.