Physics Event Classification Using Large Language Models

TL;DR

This paper describes the use of a Large Language Model, ChatGPT-3.5, to develop a binary classifier for neutrons and photons in simulated physics detector data during an AI4EIC hackathon, showcasing AI/ML applications in physics data analysis.

Contribution

It demonstrates how large language models can be employed for physics event classification tasks, integrating AI/ML techniques into experimental physics data analysis.

Findings

Successful training of a neutron-photon classifier using ChatGPT-3.5

Insights into AI/ML tool integration for physics data analysis

Engagement of international teams in AI-driven physics research

Abstract

The 2023 AI4EIC hackathon was the culmination of the third annual AI4EIC workshop at The Catholic University of America. This workshop brought together researchers from physics, data science and computer science to discuss the latest developments in Artificial Intelligence (AI) and Machine Learning (ML) for the Electron Ion Collider (EIC), including applications for detectors, accelerators, and experimental control. The hackathon, held on the final day of the workshop, involved using a chatbot powered by a Large Language Model, ChatGPT-3.5, to train a binary classifier neutrons and photons in simulated data from the \textsc{GlueX} Barrel Calorimeter. In total, six teams of up to four participants from all over the world took part in this intense educational and research event. This article highlights the hackathon challenge, the resources and methodology used, and the results and insights gained from analyzing physics data using the most cutting-edge tools in AI/ML.