Accelerating Giant Impact Simulations with Machine Learning

TL;DR

This paper introduces a machine learning model trained on extensive N-body simulation data to rapidly and accurately predict collision outcomes in planetary formation, significantly speeding up giant impact simulations.

Contribution

The authors develop a novel ML-based emulator for giant impact simulations, enabling fast and accurate predictions of collision outcomes in planetary system formation.

Findings

ML model predicts collision pairs with high accuracy

Achieves up to 10,000x speedup over traditional simulations

Enables large-scale planetary formation analyses

Abstract

Constraining planet formation models based on the observed exoplanet population requires generating large samples of synthetic planetary systems, which can be computationally prohibitive. A significant bottleneck is simulating the giant impact phase, during which planetary embryos evolve gravitationally and combine to form planets, which may themselves experience later collisions. To accelerate giant impact simulations, we present a machine learning (ML) approach to predicting collisional outcomes in multiplanet systems. Trained on more than 500,000 $N$-body simulations of three-planet systems, we develop an ML model that can accurately predict which two planets will experience a collision, along with the state of the post-collision planets, from a short integration of the system's initial conditions. Our model greatly improves on non-ML baselines that rely on metrics from dynamics theory, which struggle to accurately predict which pair of planets will experience a collision. By combining with a model for predicting long-term stability, we create an ML-based giant impact emulator, which can predict the outcomes of giant impact simulations with reasonable accuracy and a speedup of up to four orders of magnitude. We expect our model to enable analyses that would not otherwise be computationally feasible. As such, we release our training code, along with an easy-to-use API for our collision outcome model and giant impact emulator.