Fast marginalization algorithm for optimizing gravitational wave detection, parameter estimation and sky localization

Abstract

We introduce an algorithm to marginalize the likelihood for a gravitational wave signal from a quasi-circular binary merger over its extrinsic parameters, accounting for the effects of higher harmonics and spin-induced precession. The algorithm takes as input the matched-filtering time series of individual waveform harmonics against the data in all operational detectors, and the covariances of the harmonics. The outputs are the Gaussian likelihood marginalized over extrinsic parameters describing the merger time, location and orientation, along with samples from the conditional posterior of these parameters. Our algorithm exploits the waveform's known analytical dependence on extrinsic parameters to efficiently marginalize over them using a single waveform evaluation. Our current implementation achieves a 10% precision on the marginalized likelihood within $\approx 50$ ms on a single CPU core and is publicly available through the package `cogwheel`. We discuss applications of this tool for gravitational wave searches involving higher modes or precession, efficient and robust parameter estimation, and generation of sky localization maps in low latency for electromagnetic followup of gravitational-wave alerts. The inclusion of higher modes can improve the distance measurement, providing an advantage over existing low-latency localization methods.

19 pages, 7 figures

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• New search pipeline for gravitational waves with higher-order modes using mode-by-mode filtering
• Data-driven extraction, phenomenology and modeling of eccentric harmonics in binary black hole merger waveforms
• Validating Sequential Monte Carlo for Gravitational-Wave Inference
• Beyond GWTC-3: Analysing and verifying new gravitational-wave events from community catalogues
• Robust, Rapid, and Simple Gravitational-wave Parameter Estimation
• A Robust and Efficient F-statistic-based Framework for Consistent Bayesian Inference of Compact Binary Coalescences
• labrador: A domain-optimized machine-learning tool for gravitational wave inference