Gaussian processes for glitch-robust gravitational-wave astronomy

Ashton, Gregory (2023) Gaussian processes for glitch-robust gravitational-wave astronomy. Monthly Notices of the Royal Astronomical Society, 520 (2), 2983-2994. (doi:10.1093/mnras/stad341).

Record type: Article

Abstract

Interferometric gravitational-wave observatories have opened a new era in astronomy. The rich data produced by an international network enable detailed analysis of the curved space-time around black holes. With nearly 100 signals observed so far and thousands expected in the next decade, their population properties enable insights into stellar evolution and the expansion of our Universe. However, the detectors are afflicted by transient noise artefacts known as ‘glitches’ which contaminate the signals and bias inferences. Of the 90 signals detected to date, 18 were contaminated by glitches. This feasibility study explores a new approach to transient gravitational-wave data analysis using Gaussian processes, which model the underlying physics of the glitch-generating mechanism rather than the explicit realization of the glitch itself. We demonstrate that if the Gaussian process kernel function can adequately model the glitch morphology, we can recover the parameters of simulated signals. Moreover, we find that the Gaussian processes kernels used in this work are well suited to modelling long-duration glitches which are most challenging for existing glitch-mitigation approaches. Finally, we show how the time-domain nature of our approach enables a new class of time-domain tests of General Relativity, performing a re-analysis of the inspiral-merger-ringdown test on the first observed binary black hole merger. Our investigation demonstrates the feasibility of the Gaussian processes as an alternative to the traditional framework but does not yet establish them as a replacement. Therefore, we conclude with an outlook on the steps needed to realize the full potential of the Gaussian process approach.

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Accepted/In Press date: 27 January 2023

e-pub ahead of print date: 6 February 2023

Published date: 14 February 2023

Additional Information: Funding Information: We would like to sincerely thank Dan Foreman-Mackay for help with the use of the CELERITE Gaussian process python package, Colm Talbot for the suggestion of the Order Statistics approach to resolving the label-switching degeneracy, Walter Del Pozzo, Christopher Berry, and Andrew Lundgren for useful feedback on the manuscript, and Walter Del Pozzo, Marta Colleoni, Abhirup Ghosh, and Nathan Johnson-McDaniel for helpful feedback on the time-domain tests of GR. This research has used data or software obtained from the Gravitational Wave Open Science Center (gw-openscience.org), a service of LIGO Laboratory, the LIGO Scientific Collaboration, the Virgo Collaboration, and KAGRA. LIGO Laboratory and Advanced LIGO are funded by the United States National Science Foundation (NSF) as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN), and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, and Spain. The construction and operation of KAGRA are funded by Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan Society for the Promotion of Science (JSPS), National Research Foundation (NRF), and Ministry of Science and ICT (MSIT) in Korea, Academia Sinica (AS) and the Ministry of Science and Technology (MoST) in Taiwan. This work uses the SCIPY (Virtanen et al. 2020), NUMPY (Harris et al. 2020), MATPLOTLIB (Hunter 2007), GWPY (Macleod et al. 2021), and PYCBC (Nitz et al. 2017) software for data analysis and visualization.

Keywords: black hole physics, gravitational waves