@article{26cf3b7d996a483b83524b529fec663a,
title = "Gaussian processes for glitch-robust gravitational-wave astronomy",
keywords = "black hole physics, gravitational waves",
author = "Gregory Ashton",
note = "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. Publisher Copyright: {\textcopyright} 2023 The Author(s).",
year = "2023",
month = apr,
doi = "10.1093/mnras/stad341",
language = "English",
volume = "520",
pages = "2983--2994",
journal = "Monthly Notices of the Royal Astronomical Society",
issn = "0035-8711",
publisher = "Oxford University Press",
number = "2",
}