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Modelling the escape fraction of ionizing photons from galaxies in the reionization epoch: A machine learning approach
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy.
2016 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The reionization of the hydrogen in the universe represents an important but poorly understood epoch in the evolution of our universe. The most promising explanation is that radiation emitted from young hot stars in the first generation of galaxies drove the reionization. However, this theory relies on the assumption that the fraction of escaping ionizing radiation from these galaxies was high enough to sustain reionization. The upcoming James Webb Space Telescope (JWST) will enable observations of large samples of spectra from reionization-epoch galaxies. The escape fraction of ionizing photons can not be directly measured from these observations, but is predicted to have an indirect effect on the spectra of these galaxies at observable wavelengths.

In this thesis we train a machine learning algorithm known as the LASSO on simulated JWST observations, in order to obtain models which can predict escape fractions. The method studied in this thesis predicts the escape fraction with an absolute mean prediction error of 0.11 when applied to low resolution spectra with signal-to-noise ratio = 5. This prediction accuracy represents a significant improvement over previous similar methods. Our results indicate that a few spectral features exhibit the strongest effect on the escape fraction. The method shows a high level of robustness to the effects of varying levels of interstellar dust and spectral noise. A discussion which highlights some of the more problematic areas with the method is also included in this report, as well as proposed directions for future work.

Place, publisher, year, edition, pages
UPTEC F, ISSN 1401-5757 ; 16015
National Category
Engineering and Technology
URN: urn:nbn:se:uu:diva-296712OAI: oai:DiVA.org:uu-296712DiVA: diva2:939532
Educational program
Master Programme in Engineering Physics
Available from: 2016-06-20 Created: 2016-06-20 Last updated: 2016-06-20Bibliographically approved

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