Bulletin of the American Physical Society
6th Joint Meeting of the APS Division of Nuclear Physics and the Physical Society of Japan
Sunday–Friday, November 26–December 1 2023; Hawaii, the Big Island
Session L08: Minisymposium: Applications of Advanced Statistics and Machine Learning Methods in Nuclear Physics III
9:00 AM–11:00 AM,
Friday, December 1, 2023
Hilton Waikoloa Village
Room: Kohala 1
Chair: Amy Lovell, Los Alamos Natl Lab
Abstract: L08.00004 : CARIBU-matic and the MUSIC ML project: examples of machine-learning applications for beam tuning and experimental data analysis/classification*
9:45 AM–10:00 AM
Presenter:
Daniel Santiago-Gonzalez
(Argonne National Laboratory)
Authors:
Daniel Santiago-Gonzalez
(Argonne National Laboratory)
Melina Avila
(Argonne National Laboratory)
Prasanna Balaprakash
(Oak Ridge National Laboratory)
Heshani Jayatissa
(Los Alamos National Laboratory)
Krishnan Raghavan
(Argonne National Laboratory)
Nathan Callahan
(Argonne National Laboratory)
Beam tuning and experimental data analysis (or classification) are examples of time sensitive activities done at accelerator facilities. However, typical beam tuning and data classification processes are based on expert-driven manual methods which can be quite time consuming and inefficient. These deficiencies could negatively impact the outcome of an experiment. In this presentation we will discuss two separate projects that leverage machine-learning (ML) methods to optimize specific tasks in the tuning of radioactive beams from the CARIBU facility, and in the classification of data collected with the MUSIC detector.
The 1st project, CARIBU-matic, aims to develop an automated beam tuning system for the CARIBU facility, located inside the Argonne Tandem Linac Accelerator System (ATLAS), by integrating existing diagnostic detectors with a Bayesian optimization machine learning algorithm.
The 2nd project, MUSIC-ML, focuses on the strip-wise classification of specific α-induced reactions within the MUSIC detector. By combining statistical and machine learning methods, we have successfully developed a novel method for identifying these reactions [1]. This new method has been applied to two data sets produced by experiments done at the ATLAS accelerator facility involving the 17F(α,p) and 17O(α,n) reactions. Furthermore, our research demonstrates that the newly developed method outperforms several out-of-the-box techniques for outlier detection.
*This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics, under contract number DE-AC02-06CH11357. This research used resources of ANL's ATLAS facility, which is a DOE Office of Science User Facility.
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