Machine-learning clustering of close-in exoplanet populations: links to pebble accretion

arXiv:2606.11737 · astro-ph.EP, astro-ph.IM, cs.LG · Submitted 2026-06-10 · Read on arXiv

Yi Duann, Anders Johansen, Haiyang S. Wang, H. Jens Hoeijmakers

astro-ph.EP, astro-ph.IM, cs.LG

Submitted: 2026-06-10

Code: https://github.com/yiduann/gmm-exoplanet-clusters

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Clustering — 90× in this paper · explained in 2 episodes
• Core — 8× in this paper · explained in 2 episodes
• Robustness — 8× in this paper · explained in 5 episodes
• Trace — 5× in this paper · explained in 2 episodes
• Accretion Efficiency — 3× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Gaussian Mixture Model (GMM) — 3× in this paper · explained in 2 episodes
• Population Synthesis — 3× in this paper · explained in 2 episodes
• Bayesian Information Criterion — 2× in this paper
• Principal Component Analysis — 2× in this paper
• Redundancy — 2× in this paper · explained in 2 episodes
• Abundance — 1× in this paper · explained in 1 episode
• Akaike Information Criterion — 1× in this paper
• Benchmark — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Dynamical regimes — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Perplexity — 1× in this paper · explained in 3 episodes
• Principal Component Analysis (PCA) — 1× in this paper · explained in 4 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Exoplanet formation inference using conditional invertible neural networks
• Exploring the conditions for forming planetesimals by the streaming instability and planetary systems by pebble accretion

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