Gravitational wave signal recognition and ring-down time estimation via Artificial Neural Networks

Laser Interferometer Gravitational-Wave Observatory (LIGO) was the first laboratory to measure the grav-itational waves successfully. An exceptional experimental design was needed to measure distance changes less than an atomic nucleus. In the same way, the data analyses to confirm and extract information is a tremendously challenging task. This article shows a computational procedure based on Artificial Neural Networks (ANN) to recognize a black hole-black hole gravitation wave event signal from the LIGO data. With the ANN introduced methodology, it is possible to define a numerical score, like a thermometer. High score values are associated with gravitational wave observation and small values with noise. Building a time series from these scores values, physical information about the astronomical system's damping time, the ring-down time, can be estimated at a first approximation, based on a damped harmonic oscillator modeling. Here, the ring-down time is estimated, at a first approximation, with a direct data measure on the ANN score time series, without using numerical relativity techniques and high computational power.

Automated summary

This article presents a computational procedure based on Artificial Neural Networks (ANN) to recognize gravitational wave signals from LIGO data, allowing estimation of the damping time of the astronomical system through analysis of the score time series.