Denoising of gravitational wave signals via dictionary learning algorithms

TL;DR

This paper introduces the use of dictionary-learning algorithms, traditionally used in image processing, for denoising gravitational wave signals, demonstrating their effectiveness with simulated and real data.

Contribution

It is the first to apply dictionary-learning algorithms to gravitational wave denoising, using templates from numerical relativity to improve detection of low SNR signals.

Findings

Successful denoising of simulated signals embedded in Gaussian noise

Application to real GW150914 data shows promising results

Potential to enhance gravitational wave data analysis toolkit

Abstract

Gravitational wave astronomy has become a reality after the historical detections accomplished during the first observing run of the two advanced LIGO detectors. In the following years, the number of detections is expected to increase significantly with the full commissioning of the advanced LIGO, advanced Virgo and KAGRA detectors. The development of sophisticated data analysis techniques to improve the opportunities of detection for low signal-to-noise-ratio events is hence a most crucial effort. We present in this paper one such technique, dictionary-learning algorithms, which have been extensively developed in the last few years and successfully applied mostly in the context of image processing. However, to the best of our knowledge, such algorithms have not yet been employed to denoise gravitational wave signals. By building dictionaries from numerical relativity templates of both, binary black holes mergers and bursts of rotational core collapse, we show how machine-learning algorithms based on dictionaries can be also successfully applied for gravitational wave denoising. We use a subset of signals from both catalogs, embedded in non-white Gaussian noise, to assess our techniques with a large sample of tests and to find the best model parameters. The application of our method to the actual signal GW150914 shows promising results. Dictionary-learning algorithms could be a complementary addition to the gravitational wave data analysis toolkit. They may be used to extract signals from noise and to infer physical parameters if the data are in good enough agreement with the morphology of the dictionary atoms.