A Fast Bayesian Method for Coherent Gravitational Wave Searches with Relative Astrometry
Benjamin Zhang, Kris Pardo, Yijun Wang, Luke Bouma, Tzu-Ching Chang, and Olivier Dor\'e

TL;DR
This paper introduces an efficient Bayesian method for detecting coherent gravitational waves using relative astrometry, significantly reducing computational costs and providing sensitivity forecasts for space telescopes like Kepler and Roman.
Contribution
The authors extend a pulsar timing array Bayesian search technique to astrometric datasets, enabling large dataset reduction while maintaining accuracy for gravitational wave detection.
Findings
Reduced dataset size by up to 100 times with 1% accuracy
Roman can detect gravitational waves with strain above 10^{-11.4} in the 10^{-8} to 10^{-6} Hz range
Kepler can detect strains above 10^{-12.4} in the same frequency range
Abstract
Using relative stellar astrometry for the detection of coherent gravitational wave sources is a promising method for the microhertz range, where no dedicated detectors currently exist. Compared to other gravitational wave detection techniques, astrometry operates in an extreme high-baseline-number and low-SNR-per-baseline limit, which leads to computational difficulties when using conventional Bayesian search techniques. We extend a technique for efficiently searching pulsar timing array datasets through the precomputation of inner products in the Bayesian likelihood, showing that it is applicable to astrometric datasets. Using this technique, we are able to reduce the total dataset size by up to a factor of , while remaining accurate to within 1% over two orders of magnitude in gravitational wave frequency. Applying this technique to simulated astrometric datasets for…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Radio Astronomy Observations and Technology · Geophysics and Gravity Measurements
