AI-Powered Assistant for Long-Term Access to RHIC Knowledge
This work addresses the challenge of long-term access to legacy scientific data and knowledge for researchers and educators in high-energy physics, though it is incremental as it applies existing AI methods to a new domain.
The paper tackles the problem of preserving scientific knowledge and data from the Relativistic Heavy Ion Collider (RHIC) by introducing an AI-powered assistant system that provides natural language access to documentation, workflows, and software, with the result of enhancing reproducibility, education, and future discovery through deployment and integration across experiments.
As the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory concludes 25 years of operation, preserving not only its vast data holdings ($\sim$1 ExaByte) but also the embedded scientific knowledge becomes a critical priority. The RHIC Data and Analysis Preservation Plan (DAPP) introduces an AI-powered assistant system that provides natural language access to documentation, workflows, and software, with the aim of supporting reproducibility, education, and future discovery. Built upon Large Language Models using Retrieval-Augmented Generation and the Model Context Protocol, this assistant indexes structured and unstructured content from RHIC experiments and enables domain-adapted interaction. We report on the deployment, computational performance, ongoing multi-experiment integration, and architectural features designed for a sustainable and explainable long-term AI access. Our experience illustrates how modern AI/ML tools can transform the usability and discoverability of scientific legacy data.