تقرير
Machine learning based event classification for the energy-differential measurement of the $^\text{nat}$C(n,p) and $^\text{nat}$C(n,d) reactions
| العنوان: | Machine learning based event classification for the energy-differential measurement of the $^\text{nat}$C(n,p) and $^\text{nat}$C(n,d) reactions |
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| المؤلفون: | Žugec, P., Barbagallo, M., Andrzejewski, J., Perkowski, J., Colonna, N., Bosnar, D., Gawlik, A., Sabate-Gilarte, M., Bacak, M., Mingrone, F., Chiaveri, E. |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Nuclear Experiment Physics (Other) |
| مصطلحات موضوعية: | Physics - Data Analysis, Statistics and Probability, Computer Science - Computer Vision and Pattern Recognition, Nuclear Experiment |
| الوصف: | The paper explores the feasibility of using machine learning techniques, in particular neural networks, for classification of the experimental data from the joint $^\text{nat}$C(n,p) and $^\text{nat}$C(n,d) reaction cross section measurement from the neutron time of flight facility n_TOF at CERN. Each relevant $\Delta E$-$E$ pair of strips from two segmented silicon telescopes is treated separately and afforded its own dedicated neural network. An important part of the procedure is a careful preparation of training datasets, based on the raw data from Geant4 simulations. Instead of using these raw data for the training of neural networks, we divide a relevant 3-parameter space into discrete voxels, classify each voxel according to a particle/reaction type and submit these voxels to a training procedure. The classification capabilities of the structurally optimized and trained neural networks are found to be superior to those of the manually selected cuts. Comment: 11 pages, 5 figures, 2 tables |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1016/j.nima.2022.166686 |
| URL الوصول: | http://arxiv.org/abs/2204.04955 |
| رقم الأكسشن: | edsarx.2204.04955 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1016/j.nima.2022.166686 |
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