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Título: | Ariel mission planning |
Otros títulos: | Scheduling the survey of a thousand exoplanets | Autor: | Morales, Juan Carlos CSIC ORCID; Morales, Juan Carlos CSIC ORCID; Nakhjiri, Nariman CSIC ORCID; Nakhjiri, Nariman CSIC ORCID; Colomé, J. CSIC ORCID; Colomé, J. CSIC ORCID; Ribas, Ignasi CSIC ORCID; García, E.; Moreno, D.; Vilardell, Francesc CSIC ORCID | Palabras clave: | Scheduling Artificial intelligence Genetic algorithms Surveys Planetary systems |
Fecha de publicación: | 22-ene-2022 | Editor: | Springer Nature | Citación: | Experimental Astronomy 53: 807-829 (2022) | Resumen: | Automatic scheduling techniques are becoming a crucial tool for the efficient planning of large astronomical surveys. A specific scheduling method is being designed and developed for the Atmospheric Remote-sensing Infrared Exoplanet Large-survey (Ariel) mission planning based on a hybrid meta-heuristic algorithm with global optimization capability to ensure obtaining satisfying results fulfilling all mission constraints. We used this method to simulate the Ariel mission plan, to assess the feasibility of its scientific goals, and to study the outcome of different science scenarios. We conclude that Ariel will be able to fulfill the scientific objectives, i.e. characterizing ∼ 1000 exoplanet atmospheres, with a total exposure time representing about 75–80% of the mission lifetime. We demonstrate that it is possible to include phase curve observations for a sample of targets or to increase the number of studied exoplanets within the mission lifetime. Finally, around 12–15% of the time can still be used for non-time constrained observations. | Descripción: | Part of a collection: Ariel, the ESA M4 mission | Versión del editor: | http://doi.org/10.1007/s10686-021-09822-9 | URI: | http://hdl.handle.net/10261/279816 | DOI: | 10.1007/s10686-021-09822-9 | Identificadores: | doi: 10.1007/s10686-021-09822-9 issn: 1572-9508 |
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