On planetary systems as ordered sequences

TL;DR

This paper uses neural networks and linguistic models to analyze the arrangement of 4286 Kepler planets, revealing predictable patterns and categories in planetary system configurations.

Contribution

It introduces a neural network for predicting planetary properties and adapts linguistic models to categorize planetary systems, uncovering meaningful groupings.

Findings

Neural network predicts planet radius and period with 2.1 times lower MAE than naive models.

Identifies two main categories of planetary systems: compact multi-planet and around giant stars.

Planetary systems exhibit predictable, non-random patterns that inform their formation and evolution.

Abstract

A planetary system consists of a host star and one or more planets, arranged into a particular configuration. Here, we consider what information belongs to the configuration, or ordering, of 4286 Kepler planets in their 3277 planetary systems. First, we train a neural network model to predict the radius and period of a planet based on the properties of its host star and the radii and period of its neighbors. The mean absolute error of the predictions of the trained model is a factor of 2.1 better than the MAE of the predictions of a naive model which draws randomly from dynamically allowable periods and radii. Second, we adapt a model used for unsupervised part-of-speech tagging in computational linguistics to investigate whether planets or planetary systems fall into natural categories with physically interpretable "grammatical rules." The model identifies two robust groups of planetary systems: (1) compact multi-planet systems and (2) systems around giant stars ($\log{g} \lesssim 4.0$), although the latter group is strongly sculpted by the selection bias of the transit method. These results reinforce the idea that planetary systems are not random sequences -- instead, as a population, they contain predictable patterns that can provide insight into the formation and evolution of planetary systems.