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
All Reviews
AstronomyWorth Reading
beginner
At First Sight! Zero-shot Classification of Astronomical Images with Large Multimodal Models
Tanoglidis, Dimitrios & Jain, Bhuvnesh (2024)
- Published
- Oct 21, 2024
- Journal
- Research Notes of the AAS · Vol. 8 · No. 10
- DOI
- 10.3847/2515-5172/ad887a
At a GlanceAI
GPT-4o and LLaVA-NeXT classify galaxy images and artifacts above 80% accuracy using prompts alone.
SummaryAI
The study tests whether large vision-language models can classify astronomical images without being trained on astronomy-specific labels. GPT-4o and the open-source LLaVA-NeXT achieve typically above 80% accuracy for low-surface-brightness galaxies, artifacts, and galaxy morphology using natural-language prompts. The results position multimodal LLMs as potentially useful research and teaching tools, while highlighting that open models such as LLaVA-NeXT still need improvement and may benefit from domain-specific fine-tuning.
Method SnapshotAI
Evaluating GPT-4o and LLaVA-NeXT with natural-language prompts for zero-shot classification of astronomical images.
BackgroundAI
Basic knowledge of galaxy morphology, astronomical imaging, and vision-language models is helpful.
Yep, that's the point: no training, no code, just use. Similar to Smirnov (2024).
— ES