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ExoMAC SIGNED

Exoplanets Molecular Atmospheric Composition

Total Cost €

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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.

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

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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