Submission information
Submission Number: 163
Submission ID: 3620
Submission UUID: fceadae3-7184-485a-98d7-59b956feb027
Submission URI: /form/project
Created: Mon, 04/10/2023 - 14:19
Completed: Mon, 04/10/2023 - 14:19
Changed: Fri, 08/25/2023 - 08:52
Remote IP address: 131.109.33.100
Submitted by: Gaurav Khanna
Language: English
Is draft: No
Webform: Project
| Project Title | Gravitational-wave Inference with Waveform Uncertainty |
|---|---|
| Program | CAREERS |
| Project Image |
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| Tags | astrophysics (297), benchmarking (400), deep-learning (303), gravitational-waves (597), machine-learning (272) |
| Status | Complete |
| Project Leader | Michael Puerrer |
| mpuerrer@uri.edu | |
| Mobile Phone | |
| Work Phone | |
| Mentor(s) | Gaurav Khanna |
| Student-facilitator(s) | Ritesh Bachhar |
| Mentee(s) | |
| Project Description | Models of gravitational waves (GW) emitted from merging black hole binaries are based on numerical data and modeling assumptions which introduces sources of error. When performing Bayesian inference of the binary parameters from a GW signal these models are used to compute the posterior distribution with a sampling algorithm. This project aims at including marginalization over waveform uncertainty at the inference stage. The resulting posterior distribution is expected to peak closer to the true signal parameters at the cost of a slight broadening of the distribution, trading precision for improved accuracy. 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. |
| Project Deliverables | |
| Project Deliverables | |
| Student Research Computing Facilitator Profile | |
| Mentee Research Computing Profile | |
| Student Facilitator Programming Skill Level | |
| Mentee Programming Skill Level | |
| Project Institution | University of Rhode Island -- Center for Computational Research |
| Project Address | |
| Anchor Institution | CR-University of Rhode Island |
| Preferred Start Date | |
| Start as soon as possible. | No |
| Project Urgency | Already behind3Start date is flexible |
| Expected Project Duration (in months) | 3 |
| Launch Presentation | |
| Launch Presentation Date | 06/14/2023 |
| Wrap Presentation | |
| Wrap Presentation Date | 08/16/2023 |
| Project Milestones |
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| Github Contributions | |
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| What will the student learn? | |
| What will the mentee learn? | |
| What will the Cyberteam program learn from this project? | |
| HPC resources needed to complete this project? | |
| Notes | |
| What is the impact on the development of the principal discipline(s) of the project? | |
| What is the impact on other disciplines? | |
| Is there an impact physical resources that form infrastructure? | |
| Is there an impact on the development of human resources for research computing? | |
| Is there an impact on institutional resources that form infrastructure? | |
| Is there an impact on information resources that form infrastructure? | |
| Is there an impact on technology transfer? | |
| Is there an impact on society beyond science and technology? | |
| Lessons Learned | |
| Overall results |