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
AstronomyNiche
beginner
Astronomia ex machina: a history, primer and outlook on neural networks in astronomy
Smith, Michael J. & Geach, James E. (2023)
- Published
- May 1, 2023
- Journal
- Royal Society Open Science · Vol. 10 · No. 5
- DOI
- 10.1098/rsos.221454
At a GlanceAI
Review advocating open, astronomy-specific GPT-like foundation models for multimodal astronomical data.
SummaryAI
This review charts three waves of neural networks in astronomy, from early multilayer perceptrons through convolutional and recurrent networks to unsupervised and generative deep learning. It argues that rapidly growing, multimodal astronomical datasets make GPT-like foundation models a promising next step for supporting many downstream astronomy tasks. The authors propose that the astronomy community collaboratively develop open-source foundation models rather than relying solely on systems driven by large technology companies.
Method SnapshotAI
A historical and forward-looking review of neural-network methods in astronomy, culminating in a proposal for astronomy-specific foundation models.
BackgroundAI
Basic knowledge of machine learning, neural networks, and astronomical data analysis is helpful.
Has some nice theoretical stuff.
— ES