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Higher order gravitational-wave modes with likelihood reweighting

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arxiv 1905.05477 v2 pith:DLSAOQE4 submitted 2019-05-14 astro-ph.IM astro-ph.HEgr-qc

classification astro-ph.IMastro-ph.HEgr-qc
keywords modeshigher-orderordermodewaveformbayesianbinarymethod
verification ladder T0 review T1 audit T2 compute T3 formal

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The gravitational waveform of a merging stellar-mass binary is described at leading order by a quadrupolar mode. However, the complete waveform includes higher-order modes, which encode valuable information not accessible from the leading-order mode alone. Despite this, the majority of astrophysical inferences so far obtained with observations of gravitational waves employ only the leading order mode because calculations with higher-order modes are often computationally challenging. We show how to efficiently incorporate higher-order modes into astrophysical inference calculations with a two step procedure. First, we carry out Bayesian parameter estimation using a computationally cheap leading-order-mode waveform, which provides an initial estimate of binary parameters. Second, we weight the initial estimate using higher-order mode waveforms in order to fold in the extra information from the full waveform. We use mock data to demonstrate the effectiveness of this method. We apply the method to each binary black hole event in the first gravitational-wave transient catalog GWTC-1 to obtain posterior distributions and Bayesian evidence with higher-order modes. Performing Bayesian model selection on the events in GWTC-1, we find only a weak preference for waveforms with higher order modes. We discuss how this method can be generalized to a variety of other applications.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Compressive Meta-Learning

    cs.LG 2025-08 unverdicted novelty 6.0 of 10

    Meta-learned neural encoders and decoders for compressive learning aim to make parameter estimation from compact database sketches faster and more accurate than randomized, data-independent compressive learning.

  2. Searching beyond the fiducial stochastic gravitational wave background in pulsar timing array data using likelihood reweighting

    gr-qc 2025-04 conditional novelty 5.0 of 10

    A two-stage likelihood reweighting method recovers the parameters and Bayes factor of an added sinusoid signal in simulated pulsar timing data, matching full Bayesian analyses while claiming about a tenfold speedup.

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