Gravitational Wave Inference: Marginalization over Waveform Uncertainty
This study explores accurate parameter estimation of binary black holes by marginalizing over waveform uncertainty. Two approaches, Bilby and Dingo, are compared using approximated waveform models to improve the estimation process. The research aims to enhance understanding in gravitational wave data analysis through rigorous methods.
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Gravitational-wave Inference with Marginalization over Waveform Uncertainty Student: Ritesh Bachhar, University of Rhode Island riteshbachhar@uri.edu Dr. Michael P rrer, University of Rhode Mentor/Researcher: Island mpuerrer@uri.edu Date: June 14th, 2023
Gravitational Wave Albert Einstein 1916 Ripples in spacetime Accelerating massive object Black holes & Neutron Stars Warped Spacetime and Horizons of GW150914 To learn more, visit https://www.black-holes.org/gw150914 ! This movie shows the inspiral and merger of two black holes comparable to GW150914. Shown are the horizons of the black holes as black spheres, and a representation of the warped space-time geometry as the colored surface. One hemisphere of the black hole horizons is colored, highlighting the change of rotation axis during the inspiral. The height of the colored surface illustrates curvature of space, the colors from red to green indicate how much time is slowed down near black holes, and the blue and purple colors at larger distance show gravitational waves propagating away. Credit: SXS Collaboration/Canadian Institute for Theoretical Astrophysics/SciNet
Gravitational Wave Observatory Direct detection in 2015 Interferometric Observatory: LIGO-Virgo-KAGRA LIGO:Hanford Upcoming Observatory: ET; LISA Cosmic Explorer https://www.ligo.caltech.edu/image/ligo20150731f
Waveform Models Accurate waveform needed Detection and Parameter estimation Binary BH parameters: Mass, Spins, sky location etc Numerical Relativity waveforms Approximated waveforms Uncertainty in wfs causes error in estimating BBH parameters PhysRevLett.120.141103
Marginalizing of WF Uncertainty Goals Marginalizing over wf Uncertainty Accurate PE Two different approaches: Bilby: Bayesian inference code for GW data analysis Dingo: Neural network based posterior estimation Compare Peak location of posterior with the true parameter Approximated waveform model: EOB GPR
Marginalizing of WF Uncertainty Timeframe June 1st, 2023 August 31st, 2023
Marginalizing of WF Uncertainty What I hope to learn Parameter estimation of binary black holes Setting up PE workflow in Unity Parallel computing in CPU GPU: NVIDIA A100 Slurm submission script
Marginalizing of WF Uncertainty Goals for Next Month Learn tools for estimating posterior distribution of binary parameter Generate training set for Dingo ~ 5 millions waveforms Get some preliminary comparison plots
