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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AstronomySkip
intermediate
Deep Learning and Methods Based on Large Language Models Applied to Stellar Light Curve Classification
Li, Yu-Yang et al. (2025)
- Published
- Jan 1, 2025
- Journal
- Intelligent Computing · Vol. 4
- DOI
- 10.34133/icomputing.0110
At a GlanceAI
StarWhisper LC adapts language-model variants to classify stellar light curves at about 90% accuracy with little feature engineering.
SummaryAI
The study evaluates deep learning and LLM-based approaches for automatically classifying variable-star light curves from the Kepler and K2 missions. Its main novelty is the StarWhisper LC series, which fine-tunes language, multimodal-language, and audio-language models for astronomical time-series data and achieves around 90% accuracy while reducing explicit feature engineering. Conventional optimized models perform even better overall, with the Swin Transformer reaching 99% accuracy and identifying rare type II Cepheids at 83% accuracy. The accompanying analyses of cadence and phase coverage suggest that observations can be shortened or sampled less densely with limited loss of classification performance.
Method SnapshotAI
The study benchmarks optimized deep-learning architectures and fine-tuned LLM, multimodal LLM, and audio-language models for light-curve classification.
BackgroundAI
Basic machine learning and astronomy knowledge, especially variable stars and time-series light curves.