تقرير
Pulse shape discrimination using a convolutional neural network for organic liquid scintillator signals
| العنوان: | Pulse shape discrimination using a convolutional neural network for organic liquid scintillator signals |
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| المؤلفون: | Jung, K. Y., Han, B. Y., Jeon, E. J., Jeong, Y., Jo, H. S., Kim, J. Y., Kim, J. G., Kim, Y. D., Ko, Y. J., Lee, M. H., Lee, J., Moon, C. S., Oh, Y. M., Park, H. K., Seo, S. H., Seol, D. W., Siyeon, K., Sun, G. M., Yoon, Y. S., Yu, I. |
| سنة النشر: | 2022 |
| المجموعة: | High Energy Physics - Experiment Physics (Other) |
| مصطلحات موضوعية: | Physics - Instrumentation and Detectors, High Energy Physics - Experiment |
| الوصف: | A convolutional neural network (CNN) architecture is developed to improve the pulse shape discrimination (PSD) power of the gadolinium-loaded organic liquid scintillation detector to reduce the fast neutron background in the inverse beta decay candidate events of the NEOS-II data. A power spectrum of an event is constructed using a fast Fourier transform of the time domain raw waveforms and put into CNN. An early data set is evaluated by CNN after it is trained using low energy $\beta$ and $\alpha$ events. The signal-to-background ratio averaged over 1-10 MeV visible energy range is enhanced by more than 20% in the result of the CNN method compared to that of an existing conventional PSD method, and the improvement is even higher in the low energy region. Comment: 16 pages, 10 figures |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1088/1748-0221/18/03/P03003 |
| URL الوصول: | http://arxiv.org/abs/2211.07892 |
| رقم الأكسشن: | edsarx.2211.07892 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1088/1748-0221/18/03/P03003 |
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