With the Advanced LIGO and Virgo ground-based detectors consistently identifying more compact binary coalesces, the need for fast, reliable, and unbiased parameter inference is ever more vital. To that end, we introduce RIFT: an algorithm to perform Rapid parameter inference on gravitational wave sources via Iterative FiTting. To demonstrate RIFT can recover the correct parameters of coalescing compact binary systems, we compare results to the well-tested LALInference parameter inference software. We provide several examples where the unique speed and flexibility of RIFT enables otherwise intractable or awkward parameter inference analyses, such as (a) adopting costly and novel models for outgoing gravitational waves and (b) mixed-model result, each suitable to different parts of the compact binary parameter space and allowing one to use more sophisticated approximations where valid but still producing a complete posterior distribution. We also demonstrate how RIFT can be applied specifically to binary neutron stars, both for parameter inference and direct constraints on the nuclear equation of state.
We also show that two precessing models often used in inferring the properties of coalescing black hole binaries disagree substantially when sources have modestly large spins and modest mass ratios. We demonstrate these disagreements using standard figures of merit and the parameters inferred for some detections of binary black holes from O1 and O2. By comparing to numerical relativity, we confirm these disagreements reflect systematic errors. We provide concrete examples to demonstrate that these systematic errors can significantly impact inferences about astrophysically significant binary parameters.
In response to LIGO's observation of GW170104, a series of full numerical simulations of binary black holes were performed, each designed to replicate likely realizations of its dynamics and radiation. These simulations have been performed at multiple resolutions and with two independent techniques to solve Einstein's equations. For both the nonprecessing and precessing simulations, we demonstrate the two techniques agree at a precision substantially in excess of statistical uncertainties in current LIGO's observations. Conversely, we demonstrate that these full numerical solutions contain information which is not accurately captured with the approximate phenomenological models. To quantify the impact of these differences on parameter inference for GW170104 specifically, we compare the predictions of our simulations and these approximate models to LIGO's observations of GW170104.
Using one of the novel numerical relativity surrogate models, we also investigate the importance of higher order modes when inferring the parameters of coalescing compact binaries. We focus on examples relevant to the current three-detector network of observatories with a detector-frame mass set to 120$M_\odot$ and with signal amplitudes values that are consistent with plausible candidates for the next few observing runs. We show that for such systems the higher mode content will be important for interpreting coalescing binary black holes, reducing systematic bias, and computing properties of the remnant object.
Using similar tools, we finally use RIFT to analyze many real data events. This includes the loudest marginal intermediate mass binary black hole trigger from the 1st and 2nd Observing Runs as well as a subset of the events from the first half of the 3rd Observing Run. This includes both 15 binary black hole candidates and 1 binary neutron star candidate.
Library of Congress Subject Headings
Gravitational waves--Measurement; Gravitational waves--Data processing; Double stars--Structure--Data processing; Double stars--Evolution--Data processing
Astrophysical Sciences and Technology (Ph.D.)
Department, Program, or Center
School of Physics and Astronomy (COS)
Lange, Jacob, "RIFT’ing the Waves: Developing and applying an algorithm to infer properties of gravitational wave sources" (2020). Thesis. Rochester Institute of Technology. Accessed from
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Available for download on Saturday, February 27, 2021