Sampaio Alves, Catarina;
(2023)
Enabling supernova cosmology with large time-domain surveys.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
It was recently discovered that the expansion of the Universe is accelerating. Type Ia supernovae (SN Ia) were crucial for this discovery and to constrain cosmological parameters. Current and upcoming large time-domain surveys will revolutionise the field by discovering at least one order of magnitude more SNe than the currently available datasets, which will lead to tighter cosmological parameter constraints. However, these surveys will also bring challenges due to the volume of data they observe. Thus, we require new methods to analyse, understand, and extract cosmological constraints from the data. In particular, the upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) must rely on photometric classification to identify the observed SNe, instead of the traditional spectroscopic confirmation. In this thesis, we develop a methodology to perform this photometric classification based on dataset augmentation, wavelet features, and a machine learning classifier. Specifically, we find that augmenting the training set to make its features similar to the dataset we want to classify is crucial. Next, we use our methodology to measure the impact of the LSST observing strategy in photometric classification; this work contributes towards the community-focused optimisation of the observing strategy. Since the above work used simulated data, we next set a benchmark for the classification performance of our approach using the Zwicky Transient Facility real data; this is a precursor survey to LSST. Finally, we present a proof-of-concept of a neural network to predict lensed SN Ia parameters from light curves and images of those events.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Enabling supernova cosmology with large time-domain surveys |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2023. 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 Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10167857 |
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