Normalizing Flows as an Avenue to Studying Overlapping Gravitational Wave Signals

Jurriaan, Langendorff,; Kolmus, Alex; Janquart, Justin; Broeck, C. Van Den

doi:10.1103/physrevlett.130.171402

Cited by 11 publications

(3 citation statements)

References 52 publications

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“…The pivotal aspect of this network lies in the reversibility of the transformation and the straightforward computation of the Jacobian matrix. Currently, NF has found extensive application in GW signal processing [90,104,[116][117][118][119][120].…”

Section: B Normalizing Flow F Dmentioning

confidence: 99%

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

Sun 孙,

Xiong 熊,

Jin 金

et al. 2024

Chinese Phys. C

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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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Section: B Normalizing Flow F Dmentioning

confidence: 99%

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

Sun 孙,

Xiong 熊,

Jin 金

et al. 2024

Chinese Phys. C

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“…Currently, the application of deep learning in parameter inference has garnered significant attention within the GW community. Many researchers have applied deep learning models to produce the posterior for source parameters of stellar-mass BBHs [13][14][15][16][17][18][19][20][21]. Some of the models can achieve comparable performance with the MF approach on the GW events detected by LIGO/Virgo.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we present an implementation of parameter inference for nonprocessing spinning MBHBs using a deep learning model based on the normalizing flow (NF) [23]. Actually, the NF architecture has demonstrated remarkable capability in parameter inference for ground-based GW sources [16][17][18]21]. In light of this success, we make an attempt to extend its application to coalescing MBHBs detected by future space-based GW detectors.…”

Section: Introductionmentioning

confidence: 99%

Parameter Inference for Coalescing Massive Black Hole Binaries Using Deep Learning

Ruan,

Wang,

Liu

et al. 2023

Universe

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In the 2030s, a new era of gravitational wave (GW) observations will dawn as multiple space-based GW detectors, such as the Laser Interferometer Space Antenna, Taiji, and TianQin, will open the millihertz window for GW astronomy. These detectors are poised to detect a multitude of GW signals emitted by different sources. It is a challenging task for GW data analysis to recover the parameters of these sources at a low computational cost. Generally, the matched filtering approach entails exploring an extensive parameter space for all resolvable sources, incurring a substantial cost owing to the generation of GW waveform templates. To alleviate the challenge, we make an attempt to perform parameter inference for coalescing massive black hole binaries (MBHBs) using deep learning. The model trained in this work has the capability to produce 50,000 posterior samples for the redshifted total mass, mass ratio, coalescence time, and luminosity distance of an MBHB in about twenty seconds. Our model can serve as an effective data pre-processing tool, reducing the volume of parameter space by more than four orders of magnitude for MBHB signals with a signal-to-noise ratio larger than 100. Moreover, the model exhibits robustness when handling input data that contain multiple MBHB signals.

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Machine Learning Applications in Gravitational Wave Astronomy

Stergioulas

2024

Compact Objects in the Universe

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Normalizing Flows as an Avenue to Studying Overlapping Gravitational Wave Signals

Cited by 11 publications

References 52 publications

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

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

Parameter Inference for Coalescing Massive Black Hole Binaries Using Deep Learning

Machine Learning Applications in Gravitational Wave Astronomy

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