Ariel mission planning

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 $$\sim$$ ∼ 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.

Ancillary science with Ariel: Feasibility and scientific potential of young stellar object observations

“…Planning the observations of transits and eclipses of about 1000 exoplanets is a complicated problem given the large number of possible combinations and the stringent time constraint on such events. Ariel will solve this problem using an automatic scheduler based on artificial intelligence algorithms (see Morales et al, 2020, for further details), which aim to optimize the mission planning, maximizing both the number of surveyed targets, and the total time used for scientific observations. This scheduling algorithm produces a timeline of tasks by taking into account the list of exoplanets to be observed, the mission constraints and the operations that should be planned.…”

“…The timeline provided by the simulations (Morales et al, 2020) contains the start and end time of the gaps as well as the coordinates of the preceding and subsequent exoplanet transits. This way the slewing and stabilization time required for the ancillary observation can be estimated.…”

“…To investigate the plausibility of observing auxiliary targets using the waiting times, an algorithm was constructed using the received timelines (Morales et al, 2020). At each available gap the algorithm scans through the ancillary targets that are inside the field of view of the device, and assigns a value (µ i ) to each of them based on the additional slew time and stabilization time required to make the observation, the scientific importance of the target, and the time already spent observing the target.…”

“…The fact that these events occur at specific times is the main constraint for Ariel mission planning. The long-term mission planning of Ariel optimizes the observation schedule considering these transit constraints, target completeness and slewing constraints as well (Morales et al, 2017(Morales et al, , 2020. According to the simulations with this optimization, the efficiency of observations of exoplanets (the primary science targets) reaches 92%, notably above the requirements.…”

“…Allowing extra observations (i.e. observing more transits than the minimum requested for each target to increase signal-to-noise) would decrease the waiting times to ∼16% of the total time for the whole 3.5-year-long mission (0.5-4.0 yr), which is still a very substantial amount, in total ∼4900 h. According to recent scheduling results (Morales et al, 2020), approximately 1780 of these gaps would be longer than an hour, corresponding to about 3400 hours of waiting time. With efficient scheduling and target selection these waiting times could be filled with valuable observations of ancillary targets and could maximize Ariel's scientific impact.…”