Improving gravitational-wave parameter estimation using Gaussian process regression

@article{Moore2016ImprovingGP,
  title={Improving gravitational-wave parameter estimation using Gaussian process regression},
  author={C. Moore and C. Berry and A. Chua and J. Gair},
  journal={Physical Review D},
  year={2016},
  volume={93},
  pages={064001}
}
  • C. Moore, C. Berry, +1 author J. Gair
  • Published 2016
  • Physics
  • Physical Review D
  • Folding uncertainty in theoretical models into Bayesian parameter estimation is necessary in order to make reliable inferences. A general means of achieving this is by marginalizing over model uncertainty using a prior distribution constructed using Gaussian process regression (GPR). As an example, we apply this technique to the measurement of chirp mass using (simulated) gravitational-wave signals from binary black holes that could be observed using advanced-era gravitational-wave detectors… CONTINUE READING

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