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This project, Machine Learning Approaches for Accelerating Scientific Discovery in Nuclear Physics, enables collaboration between theoretical and experimental nuclear physicists and computer scientists to aid in scientific discoveries using state-of-the-art machine learning methods at the Facility for Rare Isotope Beams, the Compact Muon Solenoid experiment at CERN, Thomas Jefferson National Accelerator Facility, and the upcoming Electron Ion Collider. This NSF-funded project is an integral part of the ALPhA group (Algorithms for Learning in Physics Applications), an ongoing research collaboration headed by Professors Michelle Kuchera and Raghu Ramanujan at Davidson College.

This project has three goals:

GOAL 1: To improve data analysis methods for experiments at the Facility for Rare Isotope Beams (FRIB) by increasing analysis speed and accuracy, with special attention towards the Active-Target Time Projection Chamber, the most data-intensive of the detector systems at FRIB. and .

GOAL 2: To create novel implementations of theoretical models using generative methods informed by experimental data for use at the Thomas Jefferson National Accelerator Facility, the Electron-Ion Collider, and CERN.

GOAL 3: To optimize beam delivery techniques at FRIB and Argonne National Laboratory by investigating reinforcement learning for automatic beam tuning.

This work advances scientific understanding through research that is in line with the 2015 Long Range Plan for nuclear physics and the community-defined needs for AI in nuclear physics.

Collaborators Include:

Michelle Kuchera

Davidson College

Assistant Professor of Physics

Raghu Ramanujan

Davidson College

Associate Professor of Mathematics and Computer Science

Project Website

Related Funding

  •    NSF PHY-2012865
  •    DOE LDRD-19-13