Classifying planets by colours

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Classifying planets by colours

Post by Lazarus on 24th July 2018, 3:09 pm

Batalha et al. "Color Classification of Extrasolar Giant Planets: Prospects and Cautions"

From the conclusion:
  1. There is a strong correlation between atmospheric properties and WFIRST-like optical filters (Fig. 6). However, these correlations only truly exist for a population that does not have significant cloud coverage in the visible part of the atmosphere. If a full sample of cloud-free and cloudy planets is considered, there are less strong correlations between atmospheric properties and photometric bins that can be leveraged to classify planets.
  2. For giant planets, it is only possible to classify planets into physically motivated groups with greater than 90% accuracy if it is known a priori that the planet does not have significant cloud coverage in the visible. However, observations of Solar System and exoplanet giant planets suggest clouds are prevalent in most planetary atmospheres.
  3. Our machine learning algorithms are unable to classify planets by metallicity with greater than 55% accuracy. However, we are able to classify planets with moderate accuracy ∼70% by classifying by the cloud sedimentation efficiency, fsed. Additionally, binary classification between cloudy and cloud-free populations are successful with an accuracy >90%.
  4. We find that at least three filters are needed for any kind of classification (cloud, metallicity, etc). We also tested our classification algorithm with another filter set proposed by Krissansen-Totton et al. (2016), but do not find more optimistic results. However, our statistical algorithm is open-source and can be used to determine optimal filters for WFIRST or future mission concepts.
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