Waveform Systematics for Black Hole Binary Mergers Models (extension)

Project Title: Waveform Systematics for Black Hole Binary Mergers Models (extension)
Program:
CAREERS (323)

Project Image: {Empty}
Tags:
ai (271), AI/ML (802), astrophysics (297), conda (227), cuda (222), distributed-computing (92), jupyterhub (214), neural-networks (435)

Status: Complete
Project Leader
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Project Leader:
Michael Puerrer

Email: mpuerrer@uri.edu
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Project Personnel
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Mentor(s):
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Student-facilitator(s):
Samuel Clyne (18778)

Mentee(s):
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Project Information
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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.


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Project Deliverables:
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Student Research Computing Facilitator Profile:
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Student Facilitator Programming Skill Level: Some hands-on experience
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Project Institution: University of Rhode Island -- Center for Computational Research
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Anchor Institution: CR-University of Rhode Island
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Start as soon as possible.: No
Project Urgency: Already behind3Start date is flexible
Expected Project Duration (in months): 3
Launch Presentation: https://support.access-ci.org/system/files/webform/project/4466/Samuel_Clyne_3_month.launch.pdf
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Wrap Presentation: https://support.access-ci.org/system/files/webform/project/4466/Samuel_Clyne_3month_wrap.pptx
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Project Milestones:
- Milestone Title: Milestone #1
  Milestone Description: The student will leverage the Dingo code to train networks for precessing binaries and perform inference over a larger grid of signal parameters which now also vary over in-plane black hole spins; launch presentation. 
  Completion Date Goal: 2024-04-30
- Milestone Title: Milestone #2
  Milestone Description: The student will implement simple waveform inner products ("overlaps") between signals and template waveforms in Python and will explore whether these
  overlaps can serve to predict measures of discrepancy between posteriors such as the bias.
  Completion Date Goal: 2024-05-31
- Milestone Title: Milestone #3
  Milestone Description: The student will bring together the results generated in the study and
  will create visualizations of discrepancy measures over the signal parameter space using
  Python notebooks (matplotlib, plotly); wrap presentation.
  Completion Date Goal: 2024-06-30

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Final Report
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