Amber Boehnlein(JLAB), Paulo Bedaque(University of Maryland), Tanja Horn(Catholic University of America)
Artificial Intelligence (AI) is a rapidly developing field focused on computational technologies that can be trained, with data, to augment or automate human skill. A subset of AI is machine learning (ML), which is usually grouped into supervised, unsupervised and reinforcement learning. Nuclear Physics is big data: the gigantic data volumes produced in modern experiments now and over the next decade are reaching scales and complexities that require computational methods for tasks such as big data analytics, design of new detectors, controls, and calibration systems. AI has the potential to provide the methodologies to optimize operating parameters and perform theoretical calculations of nuclear many-body systems.
The AI4NP Winter School will give the participants a deeper understanding on what Artificial Intelligence and Machine Learning are and how they can be used to analyze nuclear physics data, design new detectors, controls, and calibration systems for nuclear physics experiments and perform theoretical calculations of nuclear many-body systems. The AI4NP lecture topics will emphasize active Nuclear Physics research, both experiment and theory, that relies on AI/ML techniques, as well as synergies between the computer science and the NP communities and inspire areas for possible collaboration in order to foster vital contributions to urgent and long-term challenges for nuclear physics.