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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.

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

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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