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On the Taxonomy of Exoplanets using Transmission Color Analysis

Presentation #207.02 in the session “Exoplanets and Systems: Data and Analysis Techniques”.

Published onOct 26, 2020
On the Taxonomy of Exoplanets using Transmission Color Analysis

The majority of exoplanets found to date have been discovered via the transit method, and transmission and emission spectra represent the primary method of studying these distant worlds. Current methods of characterizing transiting exoplanets entail the use of spectrographs on large telescopes, requiring significant observation time to study each planet. However, Crow et al (2011) showed that color-color reflectance ratios can be used to broadly categorize solar system bodies, and Sing et al (2016) and Stevenson (2016) showed trends in hot Jupiter water abundances as a function of blue-optical vs NIR/MIR altitude differences and temperature/gravity respectively. Batalha et al (2018) also showed that it is possible to classify giant planets in color-color space using WFIRST-like filters for planets that do not have significant cloud coverage. Grenfell et al (2020) went on to investigate the utility of transmission depth differences for the filters of the PLAnetary Transits and Oscilllations of stars (PLATO) mission, showing that basic atmospheric types (primary and water-dominated) and the presence of sub-micron hazes could be distinguished for some planets. Building on these concepts, we are investigating the use of transmission color-color analysis for coarse categorization of exoplanets as well as assessing the nature and habitability of these worlds, with a focus on resolving the mass/radius degeneracy to aid in discriminating super-Earths and sub-Neptunes. We will present our results, including spectrum models, model comparison frameworks, and waveband selection criteria. Preliminary results indicate the ability to distinguish between at least four different groups of mean molecular weight atmospheres (ranging from hydrogen-dominated to CO2-dominated) with greater than 80% accuracy using just a few specific low-resolution filter combinations. This method could allow for broad characterization of a large number of planets much more efficiently than current methods permit. Additionally, data collected via this method could inform follow-up observing time of large telescopes for more detailed study of worlds of interest. Finally, these data could be used to study planetary system structure for different types and ages of stars, with potentially significant impact to our understanding of planetary system formation and evolution.


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