The AI Cosmologist I: An Agentic System for Automated Data Analysis
This system automates portions of the research process for cosmologists and astronomers, potentially accelerating scientific discovery, but it is incremental as it builds on existing auto machine-learning concepts.
The authors tackled the problem of automating cosmological and astronomical data analysis by developing the AI Cosmologist, an agentic system that implements a complete pipeline from idea generation to research dissemination, and demonstrated its ability to explore solution spaces and produce scientific publications autonomously.
We present the AI Cosmologist, an agentic system designed to automate cosmological/astronomical data analysis and machine learning research workflows. This implements a complete pipeline from idea generation to experimental evaluation and research dissemination, mimicking the scientific process typically performed by human researchers. The system employs specialized agents for planning, coding, execution, analysis, and synthesis that work together to develop novel approaches. Unlike traditional auto machine-learning systems, the AI Cosmologist generates diverse implementation strategies, writes complete code, handles execution errors, analyzes results, and synthesizes new approaches based on experimental outcomes. We demonstrate the AI Cosmologist capabilities across several machine learning tasks, showing how it can successfully explore solution spaces, iterate based on experimental results, and combine successful elements from different approaches. Our results indicate that agentic systems can automate portions of the research process, potentially accelerating scientific discovery. The code and experimental data used in this paper are available on GitHub at https://github.com/adammoss/aicosmologist. Example papers included in the appendix demonstrate the system's capability to autonomously produce complete scientific publications, starting from only the dataset and task description