Digital signal analysis based on convolutional neural networks for active target time projection chambers

Autor(es)

Fortino, G. F. ; Zamora, J. C. ; Tamayose, L. E. ; Hirata, N. S. T. ; Guimaraes, V

• Tipo de documento: Artigo Científico
• Fonte: NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SP; v. 1031, p. 7-pg., 2022-05-11.

Resumo

An algorithm for digital signal analysis using convolutional neural networks (CNN) was developed in this work. The main objective of this algorithm is to make the analysis of experiments with active target time projection chambers more efficient. The code is divided in three steps: baseline correction, signal deconvolution and peak detection and integration. The CNNs were able to learn the signal processing models with relative errors of less than 6%. The analysis based on CNNs provides the same results as the traditional deconvolution algorithms, but considerably more efficient in terms of computing time (about 65 times faster). This opens up new possibilities to improve existing codes and to simplify the analysis of the large amount of data produced in active target experiments. (AU)

• Processo FAPESP: 18/04965-4 - Estudo da estrutura e reações nucleares induzidas por feixes radioativos usando alvo ativo
• Beneficiário: Juan Carlos Zamora Cardona
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 19/07767-1 - Reações nucleares com núcleos fracamente ligados ou com estrutura de cluster, radioativos e estáveis
• Beneficiário: Leandro Romero Gasques
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 16/17612-7 - Dinâmica de sistemas de muitos corpos IV
• Beneficiário: Arnaldo Gammal
• Modalidade de apoio: Auxílio à Pesquisa - Temático