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ExoMAC SIGNED

Exoplanets Molecular Atmospheric Composition

Total Cost €

0

EC-Contrib. €

0

Partnership

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 ExoMAC project word cloud

Explore the words cloud of the ExoMAC project. It provides you a very rough idea of what is the project "ExoMAC" about.

deep    absolute    active    constraints    missions    abundances    automated    light    so1    stellar    empirical    orbital    paths    date    oxygen    facilities    iac    esa    planets    neural    spanning    enric    carbon    cnn    astrofisica    handful    physical    molecules    detected    released    modern    variety    retrievals    pall    discovery    generative    stated    leverage    framework    10000    exoplanet    networks    ranges    molecular    version    curves    multiple    classification    first    atmospheres    updated    observing    individual    exomac    causes    supervision    dr    astrophysics    dex    complete    metallicity    convolutional    sampling    atmospheric    bearing    generation    characterization    exoplanets    so2    search    tess    instituto    coupled    bayesian    evolutionary    select    scientific    taurex    spectroscopic    advancing    consists    techniques    measuring    populations    4000    newly    diversity    network    context    nasa    de    instruments    eacute    transiting    architectures    code    speed    composition    precise    prime    canarias    consistent    adversarial   

Project "ExoMAC" data sheet

The following table provides information about the project.

Coordinator
INSTITUTO DE ASTROFISICA DE CANARIAS 

Organization address
address: CALLE VIA LACTEA
city: SAN CRISTOBAL DE LA LAGUNA
postcode: 38205
website: www.iac.es

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Spain [ES]
 Total cost 160˙932 €
 EC max contribution 160˙932 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2019
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2020
 Duration (year-month-day) from 2020-11-06   to  2022-11-05

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    INSTITUTO DE ASTROFISICA DE CANARIAS ES (SAN CRISTOBAL DE LA LAGUNA) coordinator 160˙932.00

Map

 Project 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.'

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The information about "EXOMAC" are provided by the European Opendata Portal: CORDIS opendata.

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