# Reliable event detection for Taylor methods in astrodynamics

arXiv: https://arxiv.org/abs/2204.09948 · HTML: https://mlsanity.com/paper/2204.09948/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2022.

## Built on by (most cited first, 5 of 5)

- [Selected Trends in Artificial Intelligence for Space Applications](https://mlsanity.com/paper/2212.06662/) (2022)
- [High-order expansion of Neural Ordinary Differential Equations flows](https://mlsanity.com/paper/2504.08769/) (2025)
- [NeuralODEs for VLEO simulations: Introducing thermoNET for Thermosphere Modeling](https://mlsanity.com/paper/2405.19384/) (2024)
- [EclipseNETs: Learning Irregular Small Celestial Body Silhouettes](https://mlsanity.com/paper/2504.04455/) (2025)
- [Comparing Behavioural Cloning and Reinforcement Learning for Spacecraft Guidance and Control Networks](https://mlsanity.com/paper/2507.19535/) (2025)

## Abstract (arXiv)

We present a novel approach for the detection of events in systems of ordinary differential equations. The new method combines the unique features of Taylor integrators with state-of-the-art polynomial root finding techniques to yield a novel algorithm ensuring strong event detection guarantees at a modest computational overhead. Detailed tests and benchmarks focused on problems in astrodynamics and celestial mechanics (such as collisional N-body systems, spacecraft dynamics around irregular bodies accounting for eclipses, computation of Poincare' sections, etc.) show how our approach is superior in both performance and detection accuracy to strategies commonly employed in modern numerical integration works. The new algorithm is available in our open source Taylor integration package heyoka.