Ariel mission planning. Scheduling the survey of a thousand exoplanets

TL;DR

This paper presents a hybrid meta-heuristic scheduling algorithm for the Ariel mission, enabling efficient planning of exoplanet surveys and demonstrating its feasibility to meet scientific goals within mission constraints.

Contribution

It introduces a novel hybrid meta-heuristic scheduling method tailored for the Ariel mission, optimizing survey planning under multiple constraints.

Findings

Ariel can characterize ~1000 exoplanet atmospheres within 75-80% of mission time.

Including phase curve observations is feasible without exceeding mission constraints.

12-15% of mission time remains for non-time constrained observations.

Abstract

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.