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ExoMAC SIGNED

Exoplanets Molecular Atmospheric Composition

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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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.

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

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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