Astronomy10 papers
LLMs in dynamical (and general) astronomy
How large language models are transforming astronomical research in celestial mechanics and dynamical astronomy.
ES
Evgeny Smirnov
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intermediate
Evaluating multimodal commercial and open-source large language models for dynamical astronomy: a benchmark study of resonant behavior classification
Smirnov, Evgeny & Carruba, Valerio (2026)
- Published
- Mar 28, 2026
- Journal
- Scientific Reports · Vol. 16 · No. 1
- DOI
- 10.1038/s41598-026-45926-y
At a GlanceAI
Benchmark shows multimodal LLMs can classify orbital resonances from images with high accuracy without fine-tuning.
SummaryAI
The study introduces reproducible benchmarks for testing multimodal LLMs on classifying mean-motion and secular resonances from plots of resonant arguments. Commercial models achieved perfect scores on unambiguous cases and up to 94% F1 on a harder three-class dataset, while open-source models approached commercial performance on the full binary benchmark. The main weakness was identifying transient and resonance-sticking behavior, but the results indicate that even untuned, locally runnable models can be useful for dynamical-astronomy classification.
Method SnapshotAI
Benchmarking multimodal commercial and open-source LLMs on image-based classification of dynamical resonance behavior.
BackgroundAI
Basic knowledge of orbital dynamics, mean-motion and secular resonances, and machine-learning evaluation metrics.
Have no resources to use the commercial models? Use OSS! You can even launch them on your laptop. And they ARE good.
— ES