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Eclipse Mapping with Ariel: Future Prospects for a Population-Level Mapping Survey

Daniel Valentine, Hannah R. Wakeford, Mark Hammond, Ryan C. Challener, Billy Edwards, Theresa Lüftinger, Maximillian N. Günther

Abstract

Eclipse mapping is a powerful tool for measuring 3D profiles of exoplanet atmospheres. To date, only JWST has been capable of widely applying this technique, but as a general observatory, it is too time-limited to conduct population-level mapping studies. Ariel, on the other hand, is a dedicated exoplanet mission set to observe 1000 transiting exoplanets, making it a natural candidate for this. To assess Ariel's mapping potential, we quantitatively benchmark its abilities against those of JWST using a simulation-and-retrieval framework with existing JWST eclipse maps as test cases. We find that for high-ranking targets, Ariel will be able to derive qualitatively similar maps to JWST using the same amount of observations; for mid-ranking targets, Ariel will be able to compete using as few as 3x as many observations; and for lower-ranking targets, the use of phase curves overcomes the need for an impractical number of repeated eclipse observations. We find that while Ariel is unlikely to have extensive latitudinal mapping abilities, it will have wide-ranging longitudinal abilities, from which the first-order atmospheric dynamics can be constrained. Using an analytically-derived metric, we determine the best eclipse mapping targets for Ariel, finding that it will be able to map nearly 100 targets using full phase curves in only quarter of its lifetime. This would be the largest mapping survey to date, and have enormous ramifications for our demographic understanding of exoplanet atmospheric dynamics. Finally, we rank all the best mapping targets for both JWST and Ariel in order to encourage future eclipse mapping studies.

Eclipse Mapping with Ariel: Future Prospects for a Population-Level Mapping Survey

Abstract

Eclipse mapping is a powerful tool for measuring 3D profiles of exoplanet atmospheres. To date, only JWST has been capable of widely applying this technique, but as a general observatory, it is too time-limited to conduct population-level mapping studies. Ariel, on the other hand, is a dedicated exoplanet mission set to observe 1000 transiting exoplanets, making it a natural candidate for this. To assess Ariel's mapping potential, we quantitatively benchmark its abilities against those of JWST using a simulation-and-retrieval framework with existing JWST eclipse maps as test cases. We find that for high-ranking targets, Ariel will be able to derive qualitatively similar maps to JWST using the same amount of observations; for mid-ranking targets, Ariel will be able to compete using as few as 3x as many observations; and for lower-ranking targets, the use of phase curves overcomes the need for an impractical number of repeated eclipse observations. We find that while Ariel is unlikely to have extensive latitudinal mapping abilities, it will have wide-ranging longitudinal abilities, from which the first-order atmospheric dynamics can be constrained. Using an analytically-derived metric, we determine the best eclipse mapping targets for Ariel, finding that it will be able to map nearly 100 targets using full phase curves in only quarter of its lifetime. This would be the largest mapping survey to date, and have enormous ramifications for our demographic understanding of exoplanet atmospheric dynamics. Finally, we rank all the best mapping targets for both JWST and Ariel in order to encourage future eclipse mapping studies.

Paper Structure

This paper contains 31 sections, 6 equations, 17 figures, 18 tables.

Figures (17)

  • Figure 1: JWST versus Ariel spectrograph bandwidths and throughputs, obtained from exotic-ld and arielrad, respectively.
  • Figure 2: HD 189733b eclipse maps, with flux profiles shown above. The cross and dashed line mark the location of the substellar point, whilst the star and dash-dotted line mark the location of the hotspot. Left: Simulated version of the JWST MIRI/LRS map, derived from two eclipses lally2025. Middle: Equivalent Ariel AIRS Ch1 map, also derived from two eclipse observations. Right: Difference map (JWST$-$Ariel). The flat structure shows that we recover consistent thermal structures between the instruments, although with a slightly shallower day-night gradient for the latter as a consequence of its smaller bandwidth (see text). The overlap of the differenced hotspot with the substellar point shows that we accurately and precisely recover this key parameter.
  • Figure 3: HD 189733b hotspot location posteriors. In blue is what we recover from the JWST MIRI/LRS map, and in orange what we recover from the Ariel AIRS Ch1 map. Using the same amount of data, Ariel is able to recover a highly consistent hotspot offset with uncertainties only $\sim$2$\times$ as large as JWST for this bright target.
  • Figure 4: HD 209458b eclipse maps, with flux profiles shown above. The cross and dashed line mark the location of the substellar point; the star and dash-dotted line mark the location of the hotspot. Left: Simulated JWST MIRI/LRS map, derived from a single eclipse observation post-processed from a GCM of the planet hd209gcm. Middle: Equivalent Ariel AIRS Ch1 map, also derived from a single eclipse observation. Right: Difference map (JWST$-$Ariel). The mostly flat structure shows that we accurately recover the JWST input map with Ariel, but with an expected shallower day-night gradient due to the smaller Ariel instrument bandwidth. The overlap of the differenced hotspot with the substellar point shows that we recover this key parameter to better than 1$\sigma$.
  • Figure 5: HD 209458b hotspot location posteriors. In blue is what we recover from the JWST MIRI/LRS map, and in orange what we recover from the Ariel AIRS Ch1 map. Using the same amount of data, and only a single eclipse observation at that, Ariel is able to recover a consistent hotspot offset as JWST with uncertainties only $\sim$3$\times$ as large for this bright target.
  • ...and 12 more figures