TPC track denoising and recognition using convolutional neural networks
- NázevTitle
- TPC track denoising and recognition using convolutional neural networksTPC track denoising and recognition using convolutional neural networks
- Druh výsledkuResult type
- Článek v časopiseJournal article
- AutořiAuthors
- M. Gajdoš, H.N. da Luz, G.G.A. Souza, M. Bregant
- Klíčová slovaKeywords
- Denoising, machine learning, Convolutional neural networks, Time Projection Chamber
- DOIDOI
- 10.1016/j.cpc.2025.109608
- Časopis / citaceJournal / citation
- Computer Physics Communications 312, 109608 (2025) · ISSN 0010-4655
- RokYear
- 2025
- JazykLanguage
- eng
- ZáznamyRecords
- ProjektProject
- Institucionální podpora na rozvoj výzkumné org.Institucionální podpora na rozvoj výzkumné org.
- CitovánoCited by
- 3 (OpenAlex)
- Citace ke staženíDownload citation
- TXT · BibTeX
AbstraktAbstract
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
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