Project description
Exploring the compositional diversity of exoplanets
The discovery of exoplanets has been one of the most significant developments in modern astronomy. In 2019, the total number of exolanets discovered reached 4 000, encompassing an impressive range of diversity. Understanding the causes of exoplanet variety is a major goal of next-generation space missions. The EU-funded ExoMAC project plans to develop new methods to measure the absolute abundances of all the main carbon- and oxygen-bearing molecules of individual planets. Detailed analysis will provide the first empirical constraints on the possible formation and evolutionary paths of exoplanets. Another goal is to develop a convolutional neural network for classifying potential exoplanets orbiting host stars relatively close to Earth. More than 10 000 transiting exoplanets are expected to be found and analysed for their composition.
Objective
"The search for and characterization of exoplanets are among the most active and rapidly advancing fields in modern astrophysics. To date, more than 4000 exoplanets have been detected, spanning wide ranges in physical, orbital and stellar parameters, and with a great variety of system architectures. Understanding the causes of exoplanet diversity and variety is a stated goal of the next-generation of ESA/NASA missions. In this context, I propose to develop the project ""Exoplanets Molecular Atmospheric Composition"" (ExoMAC), together with the Instituto de Astrofisica de Canarias (IAC) under the supervision of Dr. Enric Pallé. The project consists of the following Scientific Objectives: SO1: The complete and consistent (C&C) analyses of individual planets for measuring the absolute abundances of all the main carbon and oxygen-bearing molecules, metallicity down to less than 0.5 dex and precise C/O down to 0.1 dex in a handful of exoplanet atmospheres. The C&C analyses will provide the first empirical constraints on the possible formation and evolutionary paths of exoplanets; SO2: The development of a convolutional neural network (CNN) for the automated classification of newly-released TESS light-curves for the discovery and classification of new exoplanet populations. This CNN will lead to the discovery of more than 10000 transiting exoplanets, among which to select the prime targets for spectroscopic characterization with current and next-generation facilities. The C&C analyses propose a novel approach to leverage the information obtained with multiple instruments and observing techniques through a bayesian framework. We will adopt an updated version of the TauREx code to enable consistent retrievals, coupled with deep convolutional generative adversarial networks to speed up the likelihood sampling."
Fields of science
Keywords
Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
38205 San Cristobal De La Laguna
Spain