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From space to ground: planetary atmospheres revealed through a machine learning approach

Damiano, Mario; (2019) From space to ground: planetary atmospheres revealed through a machine learning approach. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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Abstract

In recent years, the study of exoplanetary atmospheres has flourished well beyond expectations. Current data are unveiling the key properties of hot massive planets orbiting very close to their stars, but sometimes results are not easy to interpret due to systematics affecting the data and degeneracies across the parameter space. The focus of my thesis is the study of exoplanetary atmospheres through spectroscopic observations using space and ground-based observatories. The first part of the thesis describes the development of a new pipeline to analyse the low-resolution exoplanet data recorded with the WFC3 (Wide Field Camera 3) on-board the Hubble Space Telescope (HST). The focus is on a particular dataset: HAT-P-32b which is one of the most inflated Hot-Jupiters to date. Two different approaches are presented: a more standard parametric method and the use of a machine learning technique such as independent component analysis (ICA) applied for the first time on HST dataset. Water vapour and possibly more exotic metaloxides such as VO and TiO are found in the atmosphere of HAT-P-32b. Further observations at longer wavelengths are needed to confirm these and other chemical compounds. The second part describes the development of a new pipeline to analyse high resolution datasets recorded with ground based instruments (VLT/CRIRES, TNG/GIANO-B). High-resolution spectroscopy (HRS) allows to resolve molecular bands into individual lines. Using radial velocity measurements and techniques such as Cross-Correlation Function, it is possible to separate three physically different sources: telluric absorption, stellar absorption and planetary transmission spectrum, which are normally entangled. The standard method used in the literature to analyse HRS data consists on applying corrections for the airmass and for the stellar signal and the use of ad-hoc masks to eliminate residual, strong features. The analysis method that I have developed is based on a novel use of Principal Component Analysis (PCA) that aims to maximise the planetary signal without any manual corrections. The two approaches are highly complementary and may be used to constrain the thermal structure and the composition of the planetary atmosphere.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: From space to ground: planetary atmospheres revealed through a machine learning approach
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.
Keywords: Extrasolar Planetary Atmosphere, Spectroscopy, Machine Learning
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
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/10066066
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