UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

The Emulation Game: Modelling and Machine Learning for the Epoch of Reionization

Jennings, William David; (2019) The Emulation Game: Modelling and Machine Learning for the Epoch of Reionization. Doctoral thesis (Ph.D), UCL (University College London). Green open access

[thumbnail of Jennings_000_Thesis.pdf]
Jennings_000_Thesis.pdf - Accepted version

Download (17MB) | Preview


The Epoch of Reionization (EoR) is a fascinating time in the Universe’s history. Around 400,000 years after the Big Bang, the Universe was full of neutral atoms. Over the following hundred million years or so, these atoms were slowly ionised by the first luminous objects. We have yet to make precise measurements of exactly when this process started, how long it lasted, and which types of luminous sources contributed the most. The first stars and galaxies had only just started to form, so there were precious few emission sources. The 21cm emission line of neutral hydrogen is one such source. The next generation of radio interferometers will measure for the first time three-dimensional maps of 21cm radiation during the EoR. In this thesis I present four projects for efficient modelling and analysis of the results of these EoR experiments. First I present my code for calculating higher-order clustering statistics from observed or simulated data. This code efficiently summarises useful information in the data and would allow for fast comparisons between theory and future observations. Secondly I use machine learning techniques to determine how physical EoR properties are related to the three-point clustering of simulated EoR data. Thirdly I fit an analytic clustering model to simulated 21cm maps. The model gives approximate predictions for the start of the EoR, but is unable to account for the widespread overlap of ionised regions for later times. Finally I use and compare machine learning techniques for replacing the semi-numerical simulations with trained emulators. My best emulated model makes predictions that are accurate to within 4% of the full simulation in a tiny fraction of the time.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: The Emulation Game: Modelling and Machine Learning for the Epoch of Reionization
Event: UCL (University College London)
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Copyright © The Author 2019. Original content in this thesis is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
URI: https://discovery.ucl.ac.uk/id/eprint/10085051
Downloads since deposit
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item