Background subtraction in inelastic scattering measurements using machine learning

Ghimire, R.; Ratkiewicz, A.; Pain, S. D.; Chipps, K. A.; Cizewski, J. A.; Jones, K. L.; Bedrossian, P.; Carmichael, S. R.; Garland, H.; Müller-Gatermann, Claus; Hughes, R. O.; Jayatissa, H.; Kolos, K.; Kovoor, J. M.; Kyle, A.; Reviol, W.; Richard, A.; Scielzo, N. D.; Siciliano, M.; Sims, H.; Ummel, C. C.; Williams, M.; Wilson, G. L.; Zhu, S.

doi:10.1016/j.nimb.2025.165649

Title: Background subtraction in inelastic scattering measurements using machine learning

Journal Article · 17 February 2025 · Nuclear Instruments and Methods in Physics Research. Section B, Beam Interactions with Materials and Atoms

DOI: https://doi.org/10.1016/j.nimb.2025.165649 · OSTI ID:2532510

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Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
Rutgers Univ., New Brunswick, NJ (United States)
Univ. of Tennessee, Knoxville, TN (United States)
University of Notre Dame, IN (United States)
Rutgers Univ., New Brunswick, NJ (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Argonne National Laboratory (ANL), Argonne, IL (United States)
Argonne National Laboratory (ANL), Argonne, IL (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Ohio Univ., Athens, OH (United States)
Rutgers Univ., New Brunswick, NJ (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Univ. of Surrey, Guildford (United Kingdom)
Argonne National Laboratory (ANL), Argonne, IL (United States); Louisiana State Univ., Baton Rouge, LA (United States)
Brookhaven National Laboratory (BNL), Upton, NY (United States)

Identifying, isolating, and subtracting background from the signal of interest is vital for nuclear physics experiments. These backgrounds introduce unwanted uncertainties that must be accounted for properly to extract accurate results from the signals. In nuclear reaction measurements, the typical contaminants are carbon and oxygen, contributing to background signals, and complicating the measurement of the light ejectiles. For instance, in the inelastic scattering measurement of a 20.9-MeV proton beam on ⁹⁶Mo, the ⁹⁶Mo target was contaminated with carbon and oxygen. Here, we used random forest, a machine learning algorithm commonly used for classification and regression tasks, to separate the inelastic scattering on the carbon and oxygen contaminants from the data of interest resulting from ⁹⁶Mo(p, p').

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE Office of Science (SC), Nuclear Physics (NP)

Grant/Contract Number:: AC02-06CH11357; AC05-00OR22725; AC52-07NA27344; FG02-96ER40963; FG02-96ER40983; NA0003897; NA0004066

OSTI ID:: 2532510

Report Number(s):: LLNL--JRNL-870333; 1107466

Journal Information:: Nuclear Instruments and Methods in Physics Research. Section B, Beam Interactions with Materials and Atoms, Journal Name: Nuclear Instruments and Methods in Physics Research. Section B, Beam Interactions with Materials and Atoms Vol. 561; ISSN 0168-583X

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY
Background Subtraction
Background subtraction
Inelastic Scattering
Inelastic scattering
Machine Learning
Machine learning
Nuclear science and engineering
Engineering - Instrumentation related to nuclear science and technology
Physics - Nuclear physics and radiation physics
Physics
Random Forest
Random forest
background subtraction
inelastic scattering
machine learning
nuclear physics and radiation physics
nuclear science and engineering
random forest

Title: Background subtraction in inelastic scattering measurements using machine learning

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