The Nobel prize winning discovery of gravitational waves from a binary black hole merger GW150914 opened up a new window onto the Universe. We have now seen multiple GW detections from coalescences of different kinds of compact binary objects. Accurate inference of parameters of these compact objects is a crucial part of gravitational wave astronomy. Data analysis techniques employ Bayesian statistics comparing gravitational wave models against the detected signal. Most of these models approximate solutions of Einstein's General Relativity equations, as generating numerical relativity(NR) solutions for every point in the parameter space of probable compact binary coalescences is computationally expensive. The equations are hence generally solved using analytical or semi-analytical approximations and then compared to existing NR simulations in the most nonlinear and dynamical regime. These models are subject to waveform modeling uncertainties or systematics. In this work, we provide example(s) of these systematic differences pertaining to gravitational waveform models describing mergers of compact objects and propose an efficient technique to marginalize over these differences for a given set of waveform models. We also investigate systematic differences between tidal waveform models that include higher-order modes, quantifying the differences between the inclusion and omission of higher-order modes. The marginalization technique in combination with our very efficient parameter inference algorithm RIFT, can directly account for any available models, including very accurate but computationally costly waveforms. I also describe several contributions to results performed as a part of the LIGO Scientific Collaboration, including the interpretation of the first discovered BHNS binaries.
Library of Congress Subject Headings
Gravitational waves; Double stars; Black holes (Astronomy)
Astrophysical Sciences and Technology (MS)
Department, Program, or Center
School of Physics and Astronomy (COS)
Yelikar, Anjali Balasaheb, "Waveform systematics in parameter inference of GW signals from compact binary mergers" (2021). Thesis. Rochester Institute of Technology. Accessed from
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