TPC track denoising and recognition using convolutional neural networks

Autor(es)

Gajdos, Matej ; da Luz, Hugo Natal ; Souza, Geovane G. A. ; Bregant, Marco

• Tipo de documento: Artigo Científico
• Fonte: COMPUTER PHYSICS COMMUNICATIONS; v. 312, p. 9-pg., 2025-07-01.

Resumo

The capability of convolutional neural networks to remove spurious signals caused by electronic noise, microdischarges and other effects from experimental data obtained with Time Projection Chambers is studied. A generator of synthetic data for the training of the neural network is described and its performance is compared with the results obtained with a conventional algorithm. The Physical meaning of the data resulting from the neural network and conventional denoising algorithms is thoroughly analysed, demonstrating the potential of convolutional neural networks in the preparation of raw data for analysis. (AU)

• Processo FAPESP: 20/04867-2 - Física e instrumentação de altas energias com o LHC-CERN
• Beneficiário: Marcelo Gameiro Munhoz
• Modalidade de apoio: Auxílio à Pesquisa - Projetos Especiais