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Machine Learning for Single-Ended Event Reconstruction in PROSPECT Experiment

Fuente: arXiv

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Autori principali:	Andriamirado, M., Balantekin, A. B., Bass, C. D., Rodrigues, O. Benevides, 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., Sanchez, A. Lozano, 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.
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Data Analysis, Statistics and Probability High Energy Physics - Experiment Nuclear Experiment Instrumentation and Detectors
Accesso online:	Acceder al recurso
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_version_	1866909669462638592
author	Andriamirado, M. Balantekin, A. B. Bass, C. D. Rodrigues, O. Benevides 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. Sanchez, A. Lozano 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.
author_facet	Andriamirado, M. Balantekin, A. B. Bass, C. D. Rodrigues, O. Benevides 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. Sanchez, A. Lozano 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.
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2503_06727
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Machine Learning for Single-Ended Event Reconstruction in PROSPECT Experiment Andriamirado, M. Balantekin, A. B. Bass, C. D. Rodrigues, O. Benevides 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. Sanchez, A. Lozano 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. Data Analysis, Statistics and Probability High Energy Physics - Experiment Nuclear Experiment Instrumentation and Detectors 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.
title	Machine Learning for Single-Ended Event Reconstruction in PROSPECT Experiment
topic	Data Analysis, Statistics and Probability High Energy Physics - Experiment Nuclear Experiment Instrumentation and Detectors
url	https://arxiv.org/abs/2503.06727