Speaker
Description
The Large Area Telescope (LAT) onboard NASA’s Fermi Gamma-ray Space Telescope continuously surveys the high-energy gamma-ray sky, providing observations of astrophysical phenomena ranging from variable sources and transient events to large-scale diffuse emission. Its extensive public data archive can be analysed using Fermipy, an open-source Python package that provides a high-level interface to the Fermi Science Tools and supports standard gamma-ray analysis workflows. Together, these public resources offer an ideal opportunity to explore how agentic AI systems can automate and manage complex scientific workflows.
We present the concept of Fermipylot, an AI assistant designed to guide and automate Fermi-LAT data analysis. The idea is to combine a Large Language Model (LLM) with an agentic retrieval-augmented generation workflow, grounding its decisions in Fermipy technical documentation, source code, and tutorial notebooks. From a request expressed in Natural Language (NL), Fermipylot will be able to understand the scientific goal, retrieve the relevant information, and prepare and execute a reproducible analysis. The agent will evaluate the correctness of the retrieved context and check its generated responses, while deterministic components control the execution of the scientific workflow. Assumptions, configurations, intermediate products, and results will be recorded to make each analysis transparent and reproducible.
Fermipylot will provide a case study of how LLM-based agents can connect NL interaction with established scientific software. More broadly, it will explore how AI can facilitate access to specialised research tools and support the preservation, sharing, and transfer of scientific expertise across communities and generations of researchers.