Investigating the Characteristics of Exoplanetary Atmospheres and Interiors
type: CreativeWork
description: The characterisation of exoplanets has made rapid progress in recent years, with observations of bulk properties such as mass and radius combining with detailed atmospheric spectroscopy to provide unprecedented insight into the nature of these remote worlds. However, these high-quality observations also require sophisticated modelling and analysis tools in order to maximise the scientific output from the data. In this thesis I present a number of advances in atmospheric modelling and retrieval, as well as internal structure models, which have been used to investigate the properties of a wide range of planets, from hot Jupiters to temperate mini-Neptunes. I conduct an assessment of the feasibility of supervised machine learning as a tool to carry out atmospheric retrievals of exoplanets. Retrieval methods commonly conduct Bayesian parameter estimation and statistical inference using sampling algorithms such as Markov Chain Monte Carlo or Nested Sampling. Recently several attempts have been made to use machine learning algorithms either to complement or replace fully Bayesian methods in order to improve computational efficiency. However, results from these algorithms sometimes disagree with contemporary Bayesian retrievals. To investigate this, I use the Random Forest supervised machine learning algorithm which has been applied previously for atmospheric retrieval. I extend the machine learning approach to develop a new algorithm, and demonstrate excellent agreement with a Bayesian retrieval of the transmission spectrum of the hot Jupiter HD~209458b. Despite this success, and achieving high computational efficiency, I still find that this machine learning approach is computationally prohibitive for high-dimensional parameter spaces that are routinely explored with Bayesian retrievals with modest computational resources. I discuss the trade offs and potential avenues for the future. I present \textsc{Aura-3D}, a three-dimensional atmospheric retrieval framework for exo…
abstract: The characterisation of exoplanets has made rapid progress in recent years, with observations of bulk properties such as mass and radius combining with detailed atmospheric spectroscopy to provide unprecedented insight into the nature of these remote worlds. However, these high-quality observations also require sophisticated modelling and analysis tools in order to maximise the scientific output from the data. In this thesis I present a number of advances in atmospheric modelling and retrieval, as well as internal structure models, which have been used to investigate the properties of a wide range of planets, from hot Jupiters to temperate mini-Neptunes. I conduct an assessment of the feasibility of supervised machine learning as a tool to carry out atmospheric retrievals of exoplanets. Retrieval methods commonly conduct Bayesian parameter estimation and statistical inference using sampling algorithms such as Markov Chain Monte Carlo or Nested Sampling. Recently several attempts have been made to use machine learning algorithms either to complement or replace fully Bayesian methods in order to improve computational efficiency. However, results from these algorithms sometimes disagree with contemporary Bayesian retrievals. To investigate this, I use the Random Forest supervised machine learning algorithm which has been applied previously for atmospheric retrieval. I extend the machine learning approach to develop a new algorithm, and demonstrate excellent agreement with a Bayesian retrieval of the transmission spectrum of the hot Jupiter HD~209458b. Despite this success, and achieving high computational efficiency, I still find that this machine learning approach is computationally prohibitive for high-dimensional parameter spaces that are routinely explored with Bayesian retrievals with modest computational resources. I discuss the trade offs and potential avenues for the future. I present \textsc{Aura-3D}, a three-dimensional atmospheric retrieval framework for exo…
author: Nixon, Matthew
language: eng
keywords: astrophysics, exoplanets, astronomy
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/b9d44bbe-4972-429d-8006-cf7d7898af22/download
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2022-07-22
date modified: 2026-07-22T22:24:30Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.97203