2018/06/19 by A. Torres-Forné, Alejandro Torres-Forné, Elena Cuoco +5 · 26 citations
Earth and Planetary Sciences · Physics and Astronomy · #Artificial intelligence #Astronomy #Computer science #Geophysics and Gravity Measurements #Gravitational wave #LIGO #Noise reduction #Physics #Pulsars and Gravitational Waves Research #Seismic Imaging and Inversion Techniques #Variation (astronomy) #astro-ph.IM #astro-ph.SR #gr-qc
paper · pdf · doi:10.1103/physrevd.98.084013
published in Physical review. D/Physical review. D. 98(8) (American Physical Society)
arxiv created 2018/06/19 · openalex publication_date 2018/10/09 · arxiv updated 2018/10/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We assess total-variation methods to denoise gravitational-wave signals in real noise conditions by injecting numerical-relativity waveforms from core-collapse supernovae and binary black hole mergers in data from the first observing run of Advanced LIGO. This work is an extension of our previous investigation in which only Gaussian noise was used. Since the quality of the results depends on the regularization parameter of the model, we perform a heuristic search for the value that produces the best results. We discuss various approaches for the selection of this parameter, based on the optimal, mean, or multiple values, and compare the results of the denoising upon these choices. Moreover, we also present a machine-learning-informed approach to obtain the Lagrange multiplier of the method through an automatic search. Our results provide further evidence that total-variation methods can be useful in the field of gravitational-wave astronomy as a tool to remove noise.