AI Agents as a Research Team: A Human-Supervised Case Study in Stellar Spectroscopy
astro-ph.SR, astro-ph.GA, astro-ph.IM, physics.comp-ph
Submitted: 2026-10-04
Updated: 2026-10-04
Comments: 32 pages and 11 figures. This paper showcases the power of agentic workflow in the development of research projects
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
- Generating stellar spectra using Neural Networks
- TheUse of Conditional Variational Autoencoders in Generating Stellar Spectra
- Deep Learning application for stellar parameters determination: II- Application to observed spectra of AFGK stars
- The Payne: self-consistent ab initio fitting of stellar spectra
- LRPayne: Stellar parameters and abundances from low-resolution spectra
- TransformerPayne: enhancing spectral emulation accuracy and data efficiency by capturing long-range correlations
- Agent Laboratory: Using LLM Agents as Research Assistants
- ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models
- Emergent autonomous scientific research capabilities of large language models
- SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning
- Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)
- Determining Stellar Elemental Abundances from DESI Spectra with the Data-Driven Payne
- Homogeneous Stellar Atmospheric Parameters and 22 Elemental Abundances for FGK Stars Derived From LAMOST Low-resolution Spectra with DD-Payne
- A hybrid SLAM-Payne framework for atmospheric parameter and abundance determination of early-type Stars from LAMOST DR9 low-resolution Spectra
- Observational constraints on the origin of the elements. X. Combining NLTE and machine learning for chemical diagnostics of 4 million stars in the 4MIDABLE-HR survey
- Scaling Laws for Emulation of Stellar Spectra
- PhysFormer: A Physics-Embedded Generative Model for Physically Self-Consistent Spectral Synthesis
- POSTELLAR: Posterior Stellar Spectrum Sampling - An Alternative to Approximate Stellar Spectra for Exoplanetary Analysis
- Towards an astronomical foundation model for stars with a Transformer-based model
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