Waveform Systematics for Black Hole Binary Mergers Models

Project Information

gravitational-waves
Project Status: In Progress
Project Region: CAREERS
Submitted By: Gaurav Khanna
Project Email: mpuerrer@uri.edu
Project Institution: University of Rhode Island -- Center for Computational Research
Anchor Institution: CR-University of Rhode Island

Students: Samuel Clyne

Project Description

Since the breakthrough in 2015, gravitational waves (GW) from about 90 black hole binaries
have already been observed. As GW detectors, such as LIGO, become ever more sensitive,
imperfections in the theoretical models of the GWs emitted from merging black hole binaries are
expected lead to significant biases in the estimated parameters (e.g. masses and spins) of
particularly loud GW events. This project will perform a study of such systematic effects by
leveraging the ML code "Dingo" to rapidly obtain posterior distributions for a number of relevant
waveform models. The main goal of this study is to create a visual map of measures of
discrepancies between the posteriors obtained for different waveform families for the same set
of signals.

The student will focus on learning computational tools to generate waveform datasets, train
neural networks, perform Bayesian inference with the Python-based Dingo code, compare the
resulting posterior distributions, and visualize their discrepancies on URI’s UNITY cluster.

Additional Resources

Launch Presentation:
Wrap Presentation: 6

Project Information

gravitational-waves
Project Status: In Progress
Project Region: CAREERS
Submitted By: Gaurav Khanna
Project Email: mpuerrer@uri.edu
Project Institution: University of Rhode Island -- Center for Computational Research
Anchor Institution: CR-University of Rhode Island

Students: Samuel Clyne

Project Description

Since the breakthrough in 2015, gravitational waves (GW) from about 90 black hole binaries
have already been observed. As GW detectors, such as LIGO, become ever more sensitive,
imperfections in the theoretical models of the GWs emitted from merging black hole binaries are
expected lead to significant biases in the estimated parameters (e.g. masses and spins) of
particularly loud GW events. This project will perform a study of such systematic effects by
leveraging the ML code "Dingo" to rapidly obtain posterior distributions for a number of relevant
waveform models. The main goal of this study is to create a visual map of measures of
discrepancies between the posteriors obtained for different waveform families for the same set
of signals.

The student will focus on learning computational tools to generate waveform datasets, train
neural networks, perform Bayesian inference with the Python-based Dingo code, compare the
resulting posterior distributions, and visualize their discrepancies on URI’s UNITY cluster.

Additional Resources

Launch Presentation:
Wrap Presentation: 6