Bayesian modeling of scattered light in the LIGO interferometers
Excess noise from scattered light poses a persistent challenge in the analysis of data from gravitational wave detectors such as Laser Interferometer Gravitational-wave Observatory. We integrate a physically motivated model for the behavior of these "glitches" into a standard Bayesian analysis pipeline used in gravitational wave science. This allows for the inference of the free parameters in this model, and subtraction of these models to produce glitch-free versions of the data. We show that this inference is an effective discriminator of the presence of the features of these glitches, even when those features may not be discernible in standard visualizations of the data.
© 2023 Author(s). Published under an exclusive license by AIP Publishing. The authors thank the LIGO-Virgo-KAGRA Detector Characterization and Parameter Estimation groups for their input and suggestions during the development of this work. The authors thank Arthur Tolley, Ian Harry, Andrew Lundgren, Gareth Cabourn Davies, Colm Talbot, Sophie Hourihane, and Robert Schofield for productive discussions, and also thank Colm Talbot for access to the extended Dynesty sampling methods used in this work. They also thank Andrew Lundgren for his comments during internal review of this paper. D.D. and R.P.U. are supported by the NSF as a part of the LIGO Laboratory. This material is based upon work supported by NSF's LIGO Laboratory, which is a major facility fully funded by the National Science Foundation. LIGO was constructed by the California Institute of Technology and Massachusetts Institute of Technology with funding from the National Science Foundation and operates under cooperative Agreement No. PHY-1764464. Advanced LIGO was built under Award No. PHY-0823459. The authors are grateful for computational resources provided by the LIGO Laboratory and supported by the National Science Foundation Grant Nos. PHY-0757058 and PHY-0823459. This work carries LIGO Document No. P2200350. Author Contributions. Rhiannon P. Udall: Formal analysis (lead); Investigation (equal); Methodology (equal); Visualization (equal); Writing – original draft (equal); Writing – review & editing (equal). Derek Davis: Conceptualization (lead); Investigation (equal); Methodology (equal); Visualization (equal); Writing – original draft (equal); Writing – review & editing (equal). DATA AVAILABILITY. The data that support the findings of this study are available from the corresponding author upon reasonable request. The authors have no conflicts to disclose.