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Astronomy

LLMs in dynamical astronomy

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ES

Evgeny Smirnov

2 papers

All papers in the expert’s recommended reading order. The full collection as the expert intended it.

Introduction

How large language models are transforming astronomical research in celestial mechanics and dynamical astronomy.

1
Must Read
beginner

Fast, Simple, and Accurate Time Series Analysis with Large Language Models: An Example of Mean-motion Resonances Identification(pdf)

Evgeny A. Smirnov · 2024 · The Astrophysical Journal

At a Glance

LLM can classify resonant behavior without coding or knowledge with high accuracy

Summary

The author created LLM pipeline with just one prompt that classifies resonant arguments and identifies whether time series has libration or circulation. This is a pilot study and simple cases are considered, but the accuracy and F1 score of 100% are remarkable. What's more important: instead of coding and using special methods, it takes 15m to write a prompt and use it.

Forget about filtering, periodograms, and working with time series. Just use LLMs!

— ES

Method:
LLM
Background:
Basic knowledge of LLMs
2
Niche
advanced

APBench and benchmarking large language model performance in fundamental astrodynamics problems for space engineering(pdf)

Di Wu, Raymond Zhang, Enrico M. Zucchelli et al. · 2025 · Scientific Reports

At a Glance

How good can LLMs solve space science university-level problems

Summary

Authors created a dataset of questions from Astrodynamics, tested a variety of LLMs including open-source ones on them, and evaluated their performance. The paper is a good example of a benchmark study and how to conduct it. Helpful for anyone doing benchmark stuff in astronomy.

A nice example of the usefulness of LLMs in astronomy + a good example of how to do a benchmark study in astronomy + LLM

— ES

Method:
LLM (benchmark)
Background:
Deep knowledge of benchmarking in LLMs + the state of art