Waveform Systematics for Black Hole Binary Mergers Models (extension)

Project Information

ai, AI/ML, astrophysics, conda, cuda, distributed-computing, jupyterhub, neural-networks
Project Status: Reviewing Applicants
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

This is an extension of project "Waveform Systematics for Black Hole Binary Mergers Models". That project leveraged the ML Dingo code to compute posterior distribution for gravitational wave signals and created a visual map of measures of discrepancies between the posteriors
obtained for different waveform families for the same set of signals. In this extension we aim to generalize the analysis to more generic black hole binaries sources which can undergo precession of the orbital plane and black hole spins.

Following the first project, the student will focus on training more complex neural networks, perform Bayesian inference with the Python-based Dingo code, and extend the visualizations of discrepancies between posterior distribution on URI’s UNITY cluster.

Project Information

ai, AI/ML, astrophysics, conda, cuda, distributed-computing, jupyterhub, neural-networks
Project Status: Reviewing Applicants
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

This is an extension of project "Waveform Systematics for Black Hole Binary Mergers Models". That project leveraged the ML Dingo code to compute posterior distribution for gravitational wave signals and created a visual map of measures of discrepancies between the posteriors
obtained for different waveform families for the same set of signals. In this extension we aim to generalize the analysis to more generic black hole binaries sources which can undergo precession of the orbital plane and black hole spins.

Following the first project, the student will focus on training more complex neural networks, perform Bayesian inference with the Python-based Dingo code, and extend the visualizations of discrepancies between posterior distribution on URI’s UNITY cluster.