H2020Индивидуална стипендия2020–2022

ExoMAC · Exoplanets Molecular Atmospheric Composition

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2020-11-09 → 2022-11-08
Финансиране от ЕС: 160 932 €
Участници: 1
Схема: MSCA-IF

Линиите свързват координатора с партньорите.

Накратко на български

Атмосферите на екзопланети се анализират за откриване на молекули като вода, въглероден и метанов диоксид. Това помага да се разберат начините, по които тези планети се формират и развиват в Космоса.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Exoplanets Molecular Atmospheric Composition

The ExoMAC project aimed primarily at improving our understanding of exoplanetary atmospheres by exploiting synergies between different types of observations and techniques, in particular by leveraging the information content of low- and high-resolution spectroscopic data obtained from space and ground-based telescopes (WP2: C&C Analyses). A second course of action was the discovery and characterization of new exoplanets among TESS candidates, with a focus on identifying prime targets for atmospheric observations with the JWST and Ariel space missions (WP3: TESS classification). The project aimed thus to overcome the two main limitations in the field of characterization of exoplanets: 1. The fragmentation of data analysis for individual planets that limits scientific inferences; 2. The exiguity of the statistical sample that prevents us from understanding the global picture. I addressed these two limitations through the following scientific objectives: SO1. [WP2] The complete and consistent analyses of individual planets for measuring the absolute abundances of all the main carbon and oxygen-bearing molecules (H2O, CO, CO2 and CH4), metallicity down to 0.5 dex and precise C/O down to ∼0.1 dex in a handful of exoplanet atmospheres. The C&C analyses can provide the first empirical constraints on the possible formation and evolutionary paths of exoplanets; SO2. [WP3] The development of a convolutional neural network 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 104 transiting exoplanets, among which to select the prime targets for spectroscopic characterization with current and next-generation facilities.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

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.""

Оригинален текст от CORDIS (на английски).

Участници

INSTITUTO DE ASTROFISICA DE CANARIAS · SAN CRISTOBAL DE LA LAGUNAКоординаторИспания