Machine learning for single-ended event reconstruction in PROSPECT experiment

Journal Article · Fri Aug 01 00:00:00 UTC 2025 · Journal of Instrumentation

DOI: https://doi.org/10.1088/1748-0221/20/08/P08006 · OSTI ID:3010852

Andriamirado, M. ; Balantekin, A.B. ; Bass, C.D. ; Benevides Rodrigues, O. ; Bernard, E.P. ; Bowden, N.S. ; Bryan, C.D. ; Carr, R. ; Classen, T. ; Conant, A.J. ; Deichert, G.; Delgado, A. ; Dolinski, M.J. ; Erickson, A. ; Fuller, M.; Galindo-Uribarri, A. ; Gokhale, S.; Grant, C. ; Hans, S.; Hansell, A.B. ; Haugen, T.E. ; Heeger, K.M. ; Heffron, B. ; Jaffe, D.E. ; Jayakumar, S. ; Koblanski, J. ; Kunkle, P. ; Lane, C.E. ; Littlejohn, B.R. ; Lozano Sanchez, A. ; Lu, X. ; Machado, F. ; Maricic, J. ; Mendenhall, M.P. ; Meyer, A.M. ; Milincic, R.; Mueller, P.E. ; Mumm, H.P. ; Neilson, R. ; Roca, C. ; Rosero, R.; Venegas-Vargas, D. ; Wilhelmi, J. ; Yeh, M. ; Zhang, C. ; Zhang, X.

The Precision Reactor Oscillation and Spectrum Experiment, PROSPECT, was a segmented antineutrino detector that successfully operated at the High Flux Isotope Reactor in Oak Ridge, TN, during its 2018 run. Despite challenges with photomultiplier tube base failures affecting some segments, innovative machine learning approaches were employed to perform position and energy reconstruction, and particle classification. This work highlights the effectiveness of convolutional neural networks and graph convolutional networks in enhancing data analysis. By leveraging these techniques, a 3.3% increase in effective statistics was achieved compared to traditional methods, showcasing their potential to improve analysis performance. Furthermore, these machine learning methodologies offer promising applications for other segmented particle detectors, underscoring their versatility and impact.

• Research Organization: Brookhaven National Laboratory (BNL), Upton, NY (United States); Drexel Univ., Philadelphia, PA (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: 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), High Energy Physics (HEP)
• Grant/Contract Number: AC05-00OR22725; AC52-07NA27344; SC0008347; SC0010504; SC0012704; SC0016357; SC0017660; SC0017815
• OSTI ID: 3010852
• Report Number(s): BNL--229035-2025-JAAM; LLNL--JRNL-2012433
• Journal Information: Journal of Instrumentation, Journal Name: Journal of Instrumentation Journal Issue: 08 Vol. 20; ISSN 1748-0221
• Publisher: Institute of Physics (IOP)Copyright Statement
• Country of Publication: United States
• Language: English