Low-latency parameter inference enabled by a Gaussian likelihood approximation for RIFT

2); (2) NASA Goddard Space Flight Center); A. B. Yelikar (1); R. O'Shaughnessy (1) ((1) Rochester Institute of Technology; V. Delfavero (1

Low-latency parameter inference enabled by a Gaussian likelihood approximation for RIFT

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arxiv 2301.01337 v1 pith:STXPEBQG submitted 2023-01-03 gr-qc

A. B. Yelikar (1) , V. Delfavero (1 , 2) , R. O'Shaughnessy (1) ((1) Rochester Institute of Technology , (2) NASA Goddard Space Flight Center) This is my paper · ORCID

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keywords inferenceenablelow-latencyparameterriftapproximationbinarycharacterization

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Rapid identification, characterization, and localization of gravitational waves from binary compact object mergers can enable well-informed follow-on multimessenger observations. In this work, we investigate a small modification to the RIFT parameter inference pipeline to enable extremely low-latency inference, tested here for nonprecessing sources.

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gr-qc 2026-07 accept novelty 6.0 of 10

A bilby-based joint signal-glitch inference pipeline recovers unbiased parameters in simulations and shows GW200129 spin-precession evidence is sensitive to the waveform-plus-glitch-model combination.

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