TPC track denoising and recognition using convolutional neural networks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21670%2F25%3A00383215" target="_blank" >RIV/68407700:21670/25:00383215 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.cpc.2025.109608" target="_blank" >https://doi.org/10.1016/j.cpc.2025.109608</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.cpc.2025.109608" target="_blank" >10.1016/j.cpc.2025.109608</a>

Result language
angličtina
Original language name
TPC track denoising and recognition using convolutional neural networks
Original language description
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
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10300 - Physical sciences

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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