Submission information
Submission Number: 171
Submission ID: 3898
Submission UUID: 5a8000df-b6cd-4cd7-8d2a-323b09c771c8
Submission URI: /form/project
Created: Thu, 08/03/2023 - 18:18
Completed: Thu, 08/03/2023 - 18:18
Changed: Wed, 09/04/2024 - 15:44
Remote IP address: 67.80.103.214
Submitted by: Steven Liebling
Language: English
Is draft: No
Webform: Project
| Project Title | Mapping mass and radius of compact objects to neutron star equation of state or boson star scalar potential |
|---|---|
| Program | CAREERS |
| Project Image | |
| Tags | ai (271), astrophysics (297), deep-learning (303), machine-learning (272), neural-networks (435), nvidia (527), python (69), pytorch (471) |
| Status | Halted |
| Project Leader | Steven Liebling |
| liebling@gmail.com | |
| Mobile Phone | 631 428 5035 |
| Work Phone | |
| Mentor(s) | |
| Student-facilitator(s) | Shivang Kukreja |
| Mentee(s) | |
| Project Description | Various astronomical observations provide mass and radius information about compact stellar objects that are generally thought to be neutron stars. This mass-versus-radius curve reveals information about the equation of state of the dense matter constituting the neutron star. An alternative explanation for some of these objects is that they are actually boson stars -- hypothetical compact objects made up of exotic matter. For boson stars, the mass-radius curve would reveal information about their scalar potential, the analog of a neutron star's equation of state. An important question is to what extent neutron stars and boson stars can be observationally distinguished based on their mass-versus-radius curves, or conversely to what extent these two types of object can produce the same mass-versus-radius curves. Inferring the properties of a neutron star or boson star from its mass-versus-radius curve is the inverse of the usual "forward" approach of solving the stellar structure equations with the equation of state / scalar potential to compute this curve. By parameterizing a wide class of equations of state and scalar potentials, we plan to train neural networks to predict these properties from a given mass-versus-radius curve. The question of distinguishability can then be investigated directly by studying the resulting networks and their predictions. We will first generate thousands of examples of mass-versus-radius curves corresponding to a range of neutron-star equations of state and boson-star scalar potentials by solving the Tolman-Oppenheimer-Volkoff (TOV) equations, a system of ODEs that describe the structure of these stars. We will rely on two open-source TOV solver packages for this step, though we also plan investigate a fast, GPU-enabled TOV solver written by a current graduate student at Princeton. We will then train and test neural networks that, given a mass-versus-radius curve, will predict the underlying equation of state or scalar potential that gave rise to it. The infrastructure developed in the course of this project will support future exploration of the primary scientific questions of neutron/boson star distinguishability. A previous student worked briefly on this problem and produced an initial implementation that handles a severely limited range of mass-versus-radius curves and runs on a single processor. For this project, we will need to scale both production of mass-versus-radius curves and neural network training/inference to HPC platforms. We anticipate using PyTorch for the machine-learning aspects of the project. |
| Project Deliverables | (1) a large set of mass-versus-radius curves generated from known neutron-star equations of state and boson-star scalar potentials. These will be the training and test data for our networks (2) one or more neural networks designed to infer the underlying equation of state/scalar potential from a given mass-versus-radius curve. (3) characterization of the networks on the data produced in item (1). |
| Project Deliverables | |
| Student Research Computing Facilitator Profile | We are seeking an undergraduate or graduate student with sufficient math and/or physics background to work with TOV equation solvers and sufficient Python or related programming skills to distribute the generation of mass-versus-radius curves and to implement and use neural networks in PyTorch. Some prior knowledge of applied machine learning would be a strong plus. |
| Mentee Research Computing Profile | |
| Student Facilitator Programming Skill Level | Some hands-on experience |
| Mentee Programming Skill Level | |
| Project Institution | Long Island University - Post |
| Project Address | |
| Anchor Institution | CR-Rensselaer Polytechnic Institute |
| Preferred Start Date | 10/01/2023 |
| Start as soon as possible. | Yes |
| Project Urgency | Already behind3Start date is flexible |
| Expected Project Duration (in months) | 6 |
| Launch Presentation | |
| Launch Presentation Date | |
| Wrap Presentation | |
| Wrap Presentation Date | |
| Project Milestones |
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| Github Contributions | |
| Planned Portal Contributions (if any) | |
| Planned Publications (if any) | |
| 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? | The project needs a cluster suitable for distributing generation of mass-versus-radius curves (ODE solving) and neural network training and inference. Availability of GPUs would be a strong plus, particularly if the Princeton TOV code proves feasible to use, and in any case to accelerate neural network training. |
| Notes | Potential computing resources: (1) 13-node CPU cluster local to LIU (2) Frontera (both CPU and GPU nodes) and ACCESS machines (3) the Unity cluster (URI and UMass Dartmouth) (4) RPI CCI cluster |
| 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 |