In this project the student will adapt computational inference workflows to execute on URI’s UNITY cluster and perform extensive testing using real and synthetic GW events while marginalizing over internal degrees of freedom of an effective-one-body waveform model. If time permits, the student will use the developed workflow to compare two methodologies: (i) a probabilistic Gaussian process regression (GPR) waveform model using a standard stochastic sampler, and (ii) using the novel technique of neural posterior estimation on training set data augmented with waveform uncertainties, leveraging the deep learning-based Dingo inference code.

In this project the student will adapt computational inference workflows to execute on URI’s UNITY cluster and perform extensive testing using real and synthetic GW events while marginalizing over internal degrees of freedom of an effective-one-body waveform model. If time permits, the student will use the developed workflow to compare two methodologies: (i) a probabilistic Gaussian process regression (GPR) waveform model using a standard stochastic sampler, and (ii) using the novel technique of neural posterior estimation on training set data augmented with waveform uncertainties, leveraging the deep learning-based Dingo inference code.