PIRSA:17060016

Statistical Gravitational Waveform Models: What to Simulate Next?

Doctor, Z. (2017). Statistical Gravitational Waveform Models: What to Simulate Next?. Perimeter Institute. https://pirsa.org/17060016

Doctor, Zoheyr. Statistical Gravitational Waveform Models: What to Simulate Next?. Perimeter Institute, Jun. 08, 2017, https://pirsa.org/17060016 

          @misc{ pirsa_PIRSA:17060016,
            doi = {10.48660/17060016},
            url = {https://pirsa.org/17060016},
            author = {Doctor, Zoheyr},
            keywords = {Strong Gravity},
            language = {en},
            title = {Statistical Gravitational Waveform Models: What to Simulate Next?},
            publisher = {Perimeter Institute},
            year = {2017},
            month = {jun},
            note = {PIRSA:17060016 see, \url{https://pirsa.org}}
          }

Zoheyr Doctor Northwestern University

DOI 10.48660/17060016
Collection
Talk Type Scientific Series
Subject

Abstract

Models of gravitational waveforms play a critical role in detecting and characterizing the gravitational waves (GWs) from compact binary coalescences.  Waveforms from numerical relativity (NR), while highly accurate, are too computationally expensive to produce to be directly used in parameter estimation. We propose a Gaussian process regression (GPR) method to generate accurate reduced-order-model waveforms based only on existing accurate (e.g. NR) simulations. Using a training set of simulated waveforms, our GPR approach produces interpolated waveforms along with uncertainties across the parameter space. Beyond interpolation of waveforms, we also present the ``Minimization of the Maximum Estimated Error Placement'' (MMEEP) method, which utilizes the errors provided by our GPR model to optimize the placement of future simulations.