Synergies between techniques for characterising exoplanets from space and ground-based facilities
The characterisation of exoplanets is among the most active and rapidly advancing fields in modern astrophysics. An increasing number of observing techniques have enabled the characterisation of exoplanet system properties and provided access to the planetary atmospheres (chemical composition, thermal state and dynamics). Recently, combined analyses using different types of observations have outperformed the standard approaches, e.g. enabling precise constraints on the chemical abundances and elemental ratios in their atmospheres, or measurements of both the star and planet spin-orbit angles.
The goal of this session is to inspire the cooperation between specialised teams to overcome the limits of the fragmented data analyses and to break degeneracies in their interpretation. Contributions are invited to present new methods and/or analyses that combine different kind of observations for comprehensive exoplanet characterisation.
More than 4200 exoplanets have been detected and their diversity is remarkable, ranging from very small rocky planets, to puffed gas giants. Several of their types are unknown in our Solar System, hence new classes have been defined to understand this diversity and the similarities within each group, such as their formation mechanism or core composition. We aimed to determine the main types of exoplanets, develop a method that automatically associates exoplanets to their type, classifying them into labels with a machine learning algorithm. We also worked to further understand each group, analysing their characteristics, and exploring correlations within each group. Given the planetary mass and orbital period of a large number of exoplanets, we used a K-Means clustering algorithm to classify three large groups: Hot Jupiters, Long Period Giants and Small Planets. In order to take into account more planetary and stellar parameters, we also work with the Uniform Manifold Approximation and Projection (UMAP) technique to visualize data on a 2D map, aiming to find structures within the high dimensional parameter space. We identified different clusters of exoplanets on this map with the help of groups already described in the literature. We explored how different sets of input parameters impact the clustering of exoplanets and studied, in particular, the effect of stellar metallicity. We were able to identify 5 different groups: Hot Jupiters, Longer Period Giants, sub-Jupiters, sub-Neptunes and Rocky Planets. We described these groups in terms of values for each parameter, and discussed outliers. We also analysed metallicity separately and verified that, on average, giant planets orbit around higher metallicity stars than non giant planets. The well known groups of giant exoplanets, such as Hot Jupiters and Longer Period giants, are clearly identified in the resulting UMAP 2D parameter space. For smaller planets, several groups were also visible but less separated. We also verified that the global structure is preserved, noticing, for example, the smaller planets (< 8R⨁) are grouped together and well separated from the Hot Jupiters. Adding more samples of well characterized small planets would certainly help their classification.
How to cite:
Barboza, A., Ulmer-Moll, S., and Faria, J.: Classifying Exoplanets with Machine Learning, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-833, https://doi.org/10.5194/epsc2020-833, 2020.
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