Efficient parameter inference for gravitational wave signals in the presence of transient noises using temporal and time-spectral fusion normalizing flow

Abstract

Glitches represent a category of non-Gaussian and transient noise that frequently intersects with gravitational wave (GW) signals, exerting a notable impact on the processing of GW data. The inference of GW parameters, crucial for GW astronomy research, is particularly susceptible to such interference. In this study, we pioneer the utilization of temporal and time-spectral fusion normalizing flow for likelihood-free inference of GW parameters, seamlessly integrating the high temporal resolution of the time domain with the frequency separation characteristics of both time and frequency domains. Remarkably, our findings indicate that the accuracy of this inference method is comparable to traditional non-glitch sampling techniques. Furthermore, our approach exhibits greater efficiency, boasting processing times on the order of milliseconds. In conclusion, the application of normalizing flow emerges as pivotal in handling GW signals affected by transient noises, offering a promising avenue for enhancing the field of GW astronomy research.

13 pages, 10 figures

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Cited by in corpus (12)

• The Science of the Einstein Telescope
• Robust inference of gravitational wave source parameters in the presence of noise transients using normalizing flows
• Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning
• Deep learning-driven likelihood-free parameter inference for 21-cm forest observations
• Time delay interferometry with minimal null frequencies
• Fast and accurate parameter estimation of high-redshift sources with the Einstein Telescope
• Joint inference for gravitational wave signals and glitches using a data-informed glitch model
• Search for exotic gravitational wave signals beyond general relativity using deep learning
• Residual neural networks to classify the high frequency emission in core-collapse supernova gravitational waves
• Conditional variational autoencoders for cosmological model discrimination and anomaly detection in cosmic microwave background power spectra
• Parameter inference of millilensed gravitational waves using neural spline flows
• Assessment of normalizing flows for parameter estimation on time-frequency representations of gravitational-wave data