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

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arxiv 2312.08122 v3 pith:7ZL5CI4K submitted 2023-12-13 gr-qc astro-ph.COastro-ph.IMhep-ph

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

Tian-Yang Sun , Chun-Yu Xiong , Shang-Jie Jin , Yu-Xin Wang , Jing-Fei Zhang , Xin Zhang This is my paper
classification gr-qc astro-ph.COastro-ph.IMhep-ph
keywords inferenceflownormalizingsignalstemporaltransientastronomyfrequency
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 4 Pith papers

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