gwastro / games-rapid-pe

Example of GAMES (Gravitational-wave Amortized Metric Enhanced Sampler)

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Robust, Rapid, and Simple Gravitational-wave Parameter Estimation

Introduction

Rapid and robust parameter estimation of gravitational-wave sources is a key component of modern multi-messenger astronomy. We present a novel and straightforward method for rapid parameter estimation of gravitational-wave sources that uses metric-based importance sampling. The method enables robust parameter estimation of binary neutron star and binary black hole binaries and is trivially parallelized, enabling full parameter estimation in seconds with modest resources. The algorithm achieves an average 35% effective sampling efficiency for the majority of aligned-spin neutron star binaries sources. Surprisingly, this approach is also highly efficient for analyzing the full 15-dimensional parameter space of typical binary black holes, with 20% efficiency achieved for a source detected primarily by the twin LIGO observatories and 9% for a network of three comparable sensitivity observatories. This method can serve immediate use to improve the low-latency data products of the gravitational-wave observatory network and may be a key component of how the millions of sources observed by next-generation observatories could be analyzed. The approach can also be broadly applied for problems where an approximate likelihood metric-space can be constructed.

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Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 United States License.

We encourage use of these data in derivative works. If you use the material provided here, please cite the paper using the reference:

@article{Nitz:2024nhj,
    author = "Nitz, Alexander Harvey",
    title = "{Robust, Rapid, and Simple Gravitational-wave Parameter Estimation}",
    eprint = "2410.05190",
    archivePrefix = "arXiv",
    primaryClass = "astro-ph.IM",
    month = "10",
    year = "2024"
}

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Example of GAMES (Gravitational-wave Amortized Metric Enhanced Sampler)


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