Astronomy16 papers
Artificial Intelligence and Machine Learning in Planetary Science
A companion collection for the ACM 2026 talk by Valerio Carruba
VC
Valerio Carruba
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Optimization of artificial neural networks models applied to the identification of images of asteroids’ resonant arguments
Carruba, V. et al. (2022)
- Published
- Dec 1, 2022
- Journal
- Celestial Mechanics and Dynamical Astronomy · Vol. 134 · No. 6
- DOI
- 10.1007/s10569-022-10110-7
At a GlanceAI
Optimized neural networks can identify asteroid resonant-argument image patterns, helping automate orbital dynamics analysis.
SummaryAI
The paper applies and optimizes artificial neural network models to recognize images of asteroids’ resonant arguments, a task relevant to studying orbital resonances in celestial mechanics. Its contribution is the use of tuned machine-learning models for this specialized astronomical classification problem. If effective, this approach could speed up and standardize the identification of resonant behavior in asteroid populations.
Method SnapshotAI
The study uses optimized artificial neural network models for image-based classification.
BackgroundAI
Basic knowledge of machine learning and celestial mechanics, especially asteroid orbital resonances, is helpful.
This paper applies optimized artificial neural networks to the identification of asteroid resonances, demonstrating how machine-learning models can automate a challenging classification task in celestial mechanics. Beyond its technical contribution, it introduced an innovative methodology for resonance identification and was awarded the CELMEC Prize for innovative methods in dynamical astronomy. It remains a useful reference for researchers interested in applying neural networks to dynamical astronomy problems.
— VC