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

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Astronomy

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Tang, Kwok Sun

Contributors dc:contributor

• Turk, Matthew
• Ricker, Paul
• Fields, Brian
• Narayan, Gautham

• Astronomy
• Machine Learning
• Astrophysics
• Deep Learning

Statement dc:rights

• © 2024 Kwok Sun Tang

Language dc:language

eng, en

Handle dc:identifier

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

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/124391