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P. Zugec, M. Barbagallo, J. Andrzejewski, J. Perkowski, N. Colonna, D. Bosnar, A. Gawlik, M. Sabate-Gilarte, M. Bacak, F. Mingrone, E. Chiaveri, -. n_TOF Collaboration:
"Machine learning based event classification for the energy-differential measurement of the ^natC(n,p) and ^natC(n,d) reactions";
Nuclear Instruments & Methods in Physics Research Section A, 1033 (2022), 166686; 1 - 9.

The paper explores the feasibility of using machine learning techniques, in particular neural networks, for classification of the experimental data from the joint ^natC(n,p) and ^natC(n,d) reaction cross section measurement from the neutron time of flight facility n_TOF at CERN. Each relevant ΔE-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.

Silicon telescope; Particle recognition; Machine learning; Neutron time of flight; n_TOF facility

http://dx.doi.org/10.1016/j.nima.2022.166686