Impact events

Accelerating Giant Impact Simulations With Machine Learning

By Keith Cowing

Status Report

astro-ph.EP

August 20, 2024

Filed under AI, asteroid, astro-ph.EP, Comet, exoplanet, Extinction event, impact, Impact event, Machine learning

Accelerating Giant Impact Simulations With Machine Learning — Comparison between the performance of the orbital outcome regressor (top row) and a non-ML baseline model (bottom row) on the validation set of 103,404 three-planet systems. Predicted orbital elements (i.e., semi-major axis, eccentricity, and inclination) for the new, merged planets are plotted against their true orbital elements. The ML model predicts orbital elements with somewhat less scatter and bias about the true values than the baseline model, approaching the accuracy limits imposed by chaos (see Table 1). — astro-ph.EP

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 efficient ML-based giant impact emulator, which can predict the outcomes of giant impact simulations with 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 full training code, along with an easy-to-use API for our collision outcome model and giant impact emulator.

Caleb Lammers, Miles Cranmer, Sam Hadden, Shirley Ho, Norman Murray, Daniel Tamayo

Comments: 15 pages, 7 figures, 1 table. Easy-to-use API available at this https URL
Subjects: Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG)
Cite as: arXiv:2408.08873 [astro-ph.EP] (or arXiv:2408.08873v1 [astro-ph.EP] for this version)
https://doi.org/10.48550/arXiv.2408.08873
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Submission history
From: Caleb Lammers
[v1] Fri, 16 Aug 2024 17:59:46 UTC (5,235 KB)
https://arxiv.org/abs/2408.08873

Astrobiology

Keith Cowing

Explorers Club Fellow, ex-NASA Space Station Payload manager/space biologist, Away Teams, Journalist, Lapsed climber, Synaesthete, Na’Vi-Jedi-Freman-Buddhist-mix, ASL, Devon Island and Everest Base Camp veteran, (he/him) 🖖🏻

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