Título: Um Modelo Híbrido para a Otimização Neural do Desempenho de um Calorímetro
Autores: Seixas, J. M.; Silva, P. V. M. da; Calôba, L. P.
Resumo: A neural mapping is established to improve the overall detector performance for a scintillating calorimeter, which is being designed to perform energy measurements in a next-generation high-energy collider experiment. Training a neural network with input vectors formed by the energy deposited on each cell of this granular detector, the original energy scale of the experimental particle beam is reconstructed and the linearity is significantly improved. In practice, the neural mapping corrects for nonlinearities that arise from the practical calorimeter design.
Artigo em pdf: 4cbrn_079.pdf
Arquivo BibTex: 4cbrn_079.bib