Skip to content

Construct a non-parametric framework based on Gaussian process regression to infer gravitational potential from a stellar kinematical snapshot

Notifications You must be signed in to change notification settings

YangHu99/GravitationalPotential_GPR

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

15 Commits
 
 
 
 
 
 

Repository files navigation

GravitationalPotential_GPR

UPDATE: From 27 May 2023 onwards, the dissertation draft will be removed from the repository as it is currently undergoing assessment by the Examiner at the University of Oxford. The final submitted version of the dissertation will be uploaded once the assessment process has been completed.

This repository is for an ONGOING master dissertation titled: "Non-parametric Inference of Gravitational Potential of a Galaxy from a Kinematical Snapshot". The expected finishing date would be on 29 May 2023, but so far, note that the project is still INCOMPLETE!!!

The ongoing dissertation draft contains an incomplete but self-consistent methodology on a non-parametric framework to infer the gravitational potential of a galaxy from a kinematical snapshot, where Gaussian process regression is used as the key statistical method.

The Scripts directory contains an ongoing code for an application of the framework to a toy model galaxy of isothermal disk.

Users will need to install Tensorflow and GPflow Python packages in order to run the code in this repository.

About

Construct a non-parametric framework based on Gaussian process regression to infer gravitational potential from a stellar kinematical snapshot

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published