The New Generation Planetary Population Synthesis (NGPPS)

Context. State-of-the-art planet formation models are now capable of accounting for the full spectrum of known planet types. This comes at the cost of an increasing complexity of the models, which calls into question whether established links between their initial conditions and the calculated plane...

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Veröffentlicht in:Astronomy and astrophysics (Berlin) 2021-12, Vol.656
Hauptverfasser: Schlecker, M, Pham, D, Burn, R, Alibert, Y, Mordasini, C, Emsenhuber, A, Klahr, H, Th. Henning, Mishra, L
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container_title Astronomy and astrophysics (Berlin)
container_volume 656
creator Schlecker, M
Pham, D
Burn, R
Alibert, Y
Mordasini, C
Emsenhuber, A
Klahr, H
Th. Henning
Mishra, L
description Context. State-of-the-art planet formation models are now capable of accounting for the full spectrum of known planet types. This comes at the cost of an increasing complexity of the models, which calls into question whether established links between their initial conditions and the calculated planetary observables are preserved. Aims. In this paper, we take a data-driven approach to investigate the relations between clusters of synthetic planets with similar properties and their formation history. Methods. We trained a Gaussian mixture model on typical exoplanet observables computed by a global model of planet formation to identify clusters of similar planets. We then traced back the formation histories of the planets associated with them and pinpointed their differences. Using the cluster affiliation as labels, we trained a random forest classifier to predict planet species from properties of the originating protoplanetary disk. Results. Without presupposing any planet types, we identified four distinct classes in our synthetic population. They roughly correspond to the observed populations of (sub-)Neptunes, giant planets, and (super-)Earths, plus an additional unobserved class we denote as “icy cores”. These groups emerge already within the first 0.1 Myr of the formation phase and are predicted from disk properties with an overall accuracy of >90%. The most reliable predictors are the initial orbital distance of planetary nuclei and the total planetesimal mass available. Giant planets form only in a particular region of this parameter space that is in agreement with purely analytical predictions. Including N-body interactions between the planets decreases the predictability, especially for sub-Neptunes that frequently undergo giant collisions and turn into super-Earths. Conclusions. The processes covered by current core accretion models of planet formation are largely predictable and reproduce the known demographic features in the exoplanet population. The impact of gravitational interactions highlights the need for N-body integrators for realistic predictions of systems of low-mass planets.
doi_str_mv 10.1051/0004-6361/202140551
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Using the cluster affiliation as labels, we trained a random forest classifier to predict planet species from properties of the originating protoplanetary disk. Results. Without presupposing any planet types, we identified four distinct classes in our synthetic population. They roughly correspond to the observed populations of (sub-)Neptunes, giant planets, and (super-)Earths, plus an additional unobserved class we denote as “icy cores”. These groups emerge already within the first 0.1 Myr of the formation phase and are predicted from disk properties with an overall accuracy of &gt;90%. The most reliable predictors are the initial orbital distance of planetary nuclei and the total planetesimal mass available. Giant planets form only in a particular region of this parameter space that is in agreement with purely analytical predictions. Including N-body interactions between the planets decreases the predictability, especially for sub-Neptunes that frequently undergo giant collisions and turn into super-Earths. Conclusions. The processes covered by current core accretion models of planet formation are largely predictable and reproduce the known demographic features in the exoplanet population. 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State-of-the-art planet formation models are now capable of accounting for the full spectrum of known planet types. This comes at the cost of an increasing complexity of the models, which calls into question whether established links between their initial conditions and the calculated planetary observables are preserved. Aims. In this paper, we take a data-driven approach to investigate the relations between clusters of synthetic planets with similar properties and their formation history. Methods. We trained a Gaussian mixture model on typical exoplanet observables computed by a global model of planet formation to identify clusters of similar planets. We then traced back the formation histories of the planets associated with them and pinpointed their differences. Using the cluster affiliation as labels, we trained a random forest classifier to predict planet species from properties of the originating protoplanetary disk. Results. Without presupposing any planet types, we identified four distinct classes in our synthetic population. They roughly correspond to the observed populations of (sub-)Neptunes, giant planets, and (super-)Earths, plus an additional unobserved class we denote as “icy cores”. These groups emerge already within the first 0.1 Myr of the formation phase and are predicted from disk properties with an overall accuracy of &gt;90%. The most reliable predictors are the initial orbital distance of planetary nuclei and the total planetesimal mass available. Giant planets form only in a particular region of this parameter space that is in agreement with purely analytical predictions. Including N-body interactions between the planets decreases the predictability, especially for sub-Neptunes that frequently undergo giant collisions and turn into super-Earths. Conclusions. 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subjects Clusters
Deposition
Extrasolar planets
Initial conditions
Integrators
Planet formation
Planetary nebulae
Probabilistic models
Protoplanetary disks
Species classification
title The New Generation Planetary Population Synthesis (NGPPS)
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