AI-Optimized Polarization at Jefferson Lab
The AI-Optimized Polarization project seeks to develop experimental control applications for polarized targets and beams at Jefferson Lab using AI/ML. This paper will focus on two on-going efforts involving a cryogenic polarized target and a linearly-polarized photon beam. Firstly, cryogenic targets, such as those used in Halls B and C (and approved for Hall D), are complex systems that are sensitive to a number of factors, including the temperature, beam currents, and the microwave and NMR apparatus. Secondly, the Hall D photon beam polarization depends on the optimal orientation of a diamond radiator, which produces coherent bremsstrahlung radiation from the electron beam incident upon it. Manual operation of both systems is tedious and error prone; implementing well-designed, interpretable control systems that incorporate AI is expected to lead to improved real-time polarization. AI optimization of nuclear physics experiments will lead, not just to cost-savings, but also to more efficient and higher-quality data, and this project will help to lay the foundation for future autonomous experiments.
Authors
- Malachi Schram (ORCID: https://orcid.org/0000-0002-3475-2871)
- David Lawrence (ORCID: https://orcid.org/0000-0003-0502-0847)
- C. Fanelli
- Torri Jeske (ORCID: https://orcid.org/0000-0002-5191-0501)
- Patrick Moran (ORCID: https://orcid.org/0000-0002-7110-5075)
- T. Britton (ORCID: https://orcid.org/0000-0002-3244-041X)
- N. S. Jarvis (ORCID: https://orcid.org/0000-0002-3565-7585)
- H. Egiyan (ORCID: https://orcid.org/0000-0002-5881-3616)
- James Clerk Maxwell
- J. Guo
- Chris Keith
- Armen Kasparian
Publication Details
- Published
- 2025-03-11
- DOI
- https://doi.org/10.22323/1.472.0048
- Primary Topic
- Scientific Computing and Data Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00