Pandora is an upcoming NASA SmallSat mission that will observe transiting exoplanets to study their atmospheres and the variability of their host stars. Efficient mission planning is critical for maximizing the science achieved with the year-long primary mission. To this end, we have developed a scheduler based on a metaheuristic algorithm that is focused on tackling the unique challenges of time-constrained observing missions, like Pandora. Our scheduling algorithm combines a minimum transit requirement metric, which ensures we meet observational requirements, with a “quality” metric that considers three factors to determine the scientific quality of each observation window around an exoplanet transit (defined as a visit). These three factors are: observing efficiency during a visit, the amount of the transit captured by the telescope during a visit, and how much of the transit captured is contaminated by a coincidental passing of the observatory through the South Atlantic Anomaly (SAA). The importance of each of these factors can be adjusted based on the needs or preferences of the science team. Utilizing this schedule optimizer, we develop and compare a few schedules with differing factor weights for the Pandora SmallSat mission, illustrating trade-offs that should be considered between the three quality factors. We also find that under all scenarios probed, Pandora will not only be able to achieve its observational requirements using the planets on the notional target list but will do so with significant time remaining for ancillary science.
KEYWORDS: James Webb Space Telescope, Near infrared, Atmospheric modeling, Point spread functions, Stars, Planets, Exoplanets, Atmospheric sciences, Sensors, Spectroscopy, Modeling and simulation
Pandora is a SmallSat mission, designed to study the atmospheres of exoplanets using transmission spectroscopy and to investigate the impact that stellar contamination and variability has on observing the spectra of these worlds. Pandora’s initial science operation lifetime is one year, so optimizing the science return is critical. Here we present two tools created to assist in the design process. The first is a 2-D spectrum simulator being developed to help refine target selection, optimize observation strategies, and assist in the creation of a data reduction pipeline. The second is a pseudo-retrieval framework that provides a quantifiable method for comparing potential targets against a handful of exoplanetary atmospheric parameters important to the Pandora mission. Preliminary results show Pandora will place tighter constraints on atmospheric properties like water abundance compared to HST and answering its mission objectives will help to inform targets for missions like JWST.
Pandora is a low-cost space telescope designed to measure the composition of distant transiting planets. The Pandora observatory is designed with the capability of measuring precision photometry simultaneously with nearinfrared spectroscopy, enabling scientists to disentangle stellar activity from the subtle signature of a planetary atmosphere. The broad-wavelength coverage will provide constraints on the spot and faculae covering fractions of low-mass exoplanet host stars and the impact of these active regions on exoplanetary transmission spectra. Pandora will subsequently identify exoplanets with hydrogen- or water-dominated atmospheres, and robustly determine which planets are covered by clouds and hazes. Pandora observations will also contribute to the study of transit timing variations and phase curve photometry. With a launch readiness date of early-2025, the Pandora mission represents a new class of low-cost space missions that will achieve out-of-this-world science.
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