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Glitches represent a category of non-Gaussian and transient noise thatfrequently intersects with gravitational wave (GW) signals, exerting a notableimpact on the processing of GW data. The inference of GW parameters, crucialfor GW astronomy research, is particularly susceptible to such interference. Inthis study, we pioneer the utilization of normalizing flow for likelihood-freeinference of GW parameters, seamlessly integrating the high temporal resolutionof the time domain with the frequency separation characteristics of both timeand frequency domains. Remarkably, our findings indicate that the accuracy ofthis inference method is comparable to traditional non-glitch samplingtechniques. Furthermore, our approach exhibits greater efficiency, boastingprocessing times on the order of milliseconds. In conclusion, the applicationof normalizing flow emerges as pivotal in handling GW signals affected bytransient noises, offering a promising avenue for enhancing the field of GWastronomy research.

Efficient parameter inference for gravitational wave signals in the presence of transient noises using normalizing flow