University of Illinois Urbana-Champaign

Machine learning applications in astrophysics: Reduced-order modelling for chemical kinetics and galaxy merger reconstruction with graph neural network

Tang, Kwok Sun

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TANG-DISSERTATION-2024.pdf

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https://hdl.handle.net/2142/124391

Description

Title
Machine learning applications in astrophysics: Reduced-order modelling for chemical kinetics and galaxy merger reconstruction with graph neural network
Author(s)
Tang, Kwok Sun
Issue Date
2024-04-26
Director of Research (if dissertation) or Advisor (if thesis)
Turk, Matthew
Doctoral Committee Chair(s)
Ricker, Paul
Committee Member(s)
  • Fields, Brian
  • Narayan, Gautham
Department of Study
Astronomy
Discipline
Astronomy
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
  • Astronomy
  • Machine Learning
  • Astrophysics
  • Deep Learning
Abstract
This thesis explores the application of deep learning techniques to two astrophysical problems: simplifying chemical kinetics calculations and reconstructing galaxy merger histories. A framework called Dengo is presented that can automatically generate chemical kinetics solvers from user-specified networks, enabling simplified integration of customized chemistry in simulations. The combination of neural ordinary differential equations and autoencoders is shown to be a promising approach for reducing the complexity of chemical kinetics simulations. Specifically, autoencoders identify the reduced reaction subspace while the neural ODE learn the latent space dynamics. This demonstrates the potential of using deep learning for reduced order modeling of complex chemical networks in astrophysics. For studying galaxy evolution, a conditional graph generative model is developed that can reconstruct the merger histories of observed galaxies from cosmological simulations. This allows identifying progenitors and formation pathways of galaxies across cosmic time. The model captures statistical properties of high-redshift progenitors and enables outlier detection and correlation identification. An overview of deep learning methods with a focus on techniques used in this thesis is also provided. The works demonstrate the potential of using deep learning for the selected problems in computational astrophysics and cosmic structure formation.
Graduation Semester
2024-05
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/124391
Copyright and License Information
© 2024 Kwok Sun Tang

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