Exoplanets Molecular Atmospheric Composition

Descripción del proyecto

Exploración de la diversidad en la composición de los exoplanetas

El descubrimiento de exoplanetas ha sido uno de los avances más importantes de la astronomía moderna. En 2019, el total de exoplanetas descubiertos alcanzó la cifra de 4 000, lo que abarca una gran diversidad. Uno de los objetivos fundamentales de la siguiente generación de misiones espaciales es entender las causas de la variedad de los exoplanetas. El proyecto ExoMAC, financiado con fondos europeos, tiene previsto desarrollar nuevos métodos para medir la abundancia absoluta de todas las principales moléculas portadoras de carbono y oxígeno de planetas individuales. Un análisis detallado ofrecerá las primeras restricciones empíricas a las posibles vías evolutivas y de formación de los exoplanetas. Otro objetivo consiste en desarrollar una red neuronal convolucional para clasificar exoplanetas potenciales que orbiten en torno a estrellas relativamente cercanas a la Tierra. Se espera encontrar y analizar más de diez mil exoplanetas en tránsito para conocer su composición.

Objetivo

"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."