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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.

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

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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