Machine Learning for Nuclear Physics and the Electron Ion Collider (HUGS2023)
Machine Learning for Nuclear Physics and the Electron Ion Collider (HUGS2023)#
This website hosts a mini-series of lectures on AI/ML for Nuclear Physics and the Electron Ion Collider, taught at HUGS2023.
This is the introduction page. You can navigate the lectures contained in this book.
Synopsis: The HUGS2023 lectures on Machine Learning for Nuclear Physics and the Electron Ion Collider are a focused 6-hour program designed to introduce physics graduate students to the fundamentals of artificial intelligence and machine learning (AI/ML), and their applications in nuclear physics (NP), specifically concerning the Electron Ion Collider (EIC) project.
The course aims to equip students with a basic understanding of AI/ML basics, and how these techniques can be utilized to interpret and analyze NP data. A key component of these lectures is exploring the role of AI/ML in making sense of the datasets anticipated from the EIC project.
The students will gain a practical understanding of AI/ML concepts through hands-on exercises using simulated NP data. The lectures serve as a launchpad for those aspiring to integrate AI/ML techniques into their NP research while also offering valuable insights into some of the latest AI/ML initiatives ongoing at both the JLab and EIC.
This course is based on the following references [1, 2, 3, 4, 5, 6]
Lectures
- (Lec 1) AI/ML for Nuclear Physics - A High-Bias, Low-Variance Introduction
- (Lec 2) Leveraging AI/ML for the future Electron Ion Collider
- (Lec 2, extra) AI/ML activities at Jefferson Lab - Paving the way for the Electron Ion Collider
- (Lec 3) ePIC at EIC – The First Large-Scale Experiment Designed with the AI Assistance
Tutorials
Other References
Datasets / Modules
References