Augmented kludge waveforms and Gaussian process regression for EMRI data analysis

Abstract

Extreme-mass-ratio inspirals (EMRIs) will be an important type of astrophysical source for future space-based gravitational-wave detectors. There is a trade-off between accuracy and computational speed for the EMRI waveform templates required in the analysis of data from these detectors. We discuss how the systematic error incurred by using faster templates may be reduced with improved models such as augmented kludge waveforms, and marginalised over with statistical techniques such as Gaussian process regression.

4 pages, 2 figures, conference proceedings for 11th Edoardo Amaldi Conference on Gravitational Waves

References in corpus (4)

• Improved analytic extreme-mass-ratio inspiral model for scoping out eLISA data analysis
• Improving gravitational-wave parameter estimation using Gaussian process regression
• Calculation of radiation reaction effect on orbital parameters in Kerr spacetime
• Novel Method for Incorporating Model Uncertainties into Gravitational Wave Parameter Estimates

Cited by in corpus (7)

• Science with the TianQin observatory: Preliminary result on extreme-mass-ratio inspirals
• Science with the TianQin Observatory: Preliminary Results on Testing the No-hair Theorem with EMRI
• Detecting Gravitational-waves from Extreme Mass Ratio Inspirals using Convolutional Neural Networks
• 2 Fast 2 Fiducial: Gaussian processes for the interpolation and marginalization of waveform error in extreme-mass-ratio-inspiral parameter estimation
• Identification of Gravitational-waves from Extreme Mass Ratio Inspirals
• Improving the scalability of Gaussian-process error marginalization in gravitational-wave inference
• Augmented analytic kludge waveform with quadrupole moment correction