Keil, Marcus;
(2025)
Statistical analyses of astrochemical Big Data.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
This thesis presents original research and development of statistical tools for astrochemical inferences of observations using modelling tools. Funded through the AstroChemical Origin (ACO) Innovative Training Network (ITN), the work of this thesis specialises in the intersection of statistical software engineering and astrochemistry to complement the interdisciplinary research being conducted in the ACO ITN, which includes work in radio instrumentation, astronomical observations, and astrochemical modelling, as well as experimental and theoretical chemistry. The work in chapter 2 describes the statistical inference tool UCLCHEMCMC, developed so that observers could perform advanced modelling of their molecular observations without needing detailed knowledge on how to model. The approach uses a database in order to reduce computational time while also presenting future opportunities to train machine learning algorithms with the collected data. Chapter 3 describes the work done using UCLCHEMCMC in order to study which physical parameters influence the observable sulphur in the interstellar medium the most. This work aimed to not only study why sulphur is not observed at the expected abundances but also to showcase the broad applications of the UCLCHEMCMC tool beyond the standard inferences. The penultimate chapter, chapter 4, describes the ACO outreach project, which was made to be a virtual reality experience. The experience is aimed at encouraging secondary school students to learn more about astrochemistry, specifically the journey of water, in order to help inspire the next generation of researchers. The development of this project follows more closely to that of a video game being developed, as education through entertainment will create a desire for learning in students. The last chapter describes future projects already planned, some of which are already being worked on. It starts by describing the changes that need to be made to UCLCHEMCMC in order to allow an online version to be available to the public, rather than just downloading the source code. The second part of the last chapter describes the prototype dashboard being developed for astronomical archival data, its potential challenges, and uses. Appendix A describes work that is not directly related to astrophysics but has applications to astrophysics. This work was performed in a healthcare research group with the aim to set up a data storage solution and data dashboard that would aid a healthcare research group to improve their methods for research. The development of a data storage solution and dashboard in a healthcare setting inspired work to be completed in the future to create a dashboard for astronomical survey data.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Statistical analyses of astrochemical Big Data |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2025. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/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 BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10208757 |
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