Glitch subtraction from gravitational wave data using adaptive spline fitting

Abstract

Transient signals of instrumental and environmental origins ("glitches") in gravitational wave data elevate the false alarm rate of searches for astrophysical signals and reduce their sensitivity. Glitches that directly overlap astrophysical signals hinder their detection and worsen parameter estimation errors. As the fraction of data occupied by detectable astrophysical signals will be higher in next generation detectors, such problematic overlaps could become more frequent. These adverse effects of glitches can be mitigated by estimating and subtracting them out from the data, but their unpredictable waveforms and large morphological diversity pose a challenge. Subtraction of glitches using data from auxiliary sensors as predictors works but not for the majority of cases. Thus, there is a need for nonparametric glitch mitigation methods that do not require auxiliary data, work for a large variety of glitches, and have minimal effect on astrophysical signals in the case of overlaps. In order to cope with the high rate of glitches, it is also desirable that such methods be computationally fast. We show that adaptive spline fitting, in which the placement of free knots is optimized to estimate both smooth and non-smooth curves in noisy data, offers a promising approach to satisfying these requirements for broadband short-duration glitches, the type that appear quite frequently. The method is demonstrated on glitches drawn from three distinct classes in the Gravity Spy database as well as on the glitch that overlapped the binary neutron star signal GW170817. The impact of glitch subtraction on the GW170817 signal, or those like it injected into the data, is seen to be negligible.

17 pages, 7 figures, revised version (new figure added)

References in corpus (14)

• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• Advanced Virgo: a 2nd generation interferometric gravitational wave detector
• Advanced LIGO
• GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run
• Exploring the Sensitivity of Next Generation Gravitational Wave Detectors
• BayesWave: Bayesian Inference for Gravitational Wave Bursts and Instrument Glitches
• Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
• The LIGO Open Science Center
• A Gravitational Wave Detector with Cosmological Reach
• Machine-learning non-stationary noise out of gravitational wave detectors
• Subtracting glitches from gravitational-wave detector data during the third observing run
• Modeling compact binary signals and instrumental glitches in gravitational wave data
• Transient glitch mitigation in Advanced LIGO data with $\text{glitschen}$
• Spline Based Search Method For Unmodeled Transient Gravitational Wave Chirps

Cited by in corpus (4)

• Efficient parameter inference for gravitational wave signals in the presence of transient noises using temporal and time-spectral fusion normalizing flow
• Robust inference of gravitational wave source parameters in the presence of noise transients using normalizing flows
• Joint inference for gravitational wave signals and glitches using a data-informed glitch model
• Glitch veto based on unphysical gravitational wave binary inspiral templates