At the High Energy Accelerator Research Organization (KEK) in Tsukuba, Japan, the double-sided silicon strip sub-detector of the Belle II experiment is read out by 1748 APV25 chips. FPGAs perform several calculations on the digitized signals. One of them will be "Hit Time Finding": the determination of the time and amplitude of the signal peaks of each event in real time using pre-programmed neural networks. This work analyses the possibility, precision and reliability of these calculations depending on various parameters.
Machine learning: hit time finding with a neural network / Thalmeier, R., Yin, H., Aihara, H., Aziz, T., Bacher, S., Bahinipati, S., Barberio, E., Baroncelli, Ti., Baroncelli, To., Basith, A.K., Batignani, G., Bauer, A., Behera, P.K., Bertacchi, V., Bettarini, S., Bhuyan, B., Bilka, T., Bosi, F., Bosisio, L., Bozek, A., et al.. - In: POS PROCEEDINGS OF SCIENCE. - ISSN 1824-8039. - (2019). (Topical Workshop on Electronics for Particle Physics (TWEPP2018) Antwerp; Belgium ) [10.22323/1.343.0065].
Machine learning: hit time finding with a neural network
Batignani, Giovanni;Forti, F.;Lanceri, L.;Paladino, A.;
2019
Abstract
At the High Energy Accelerator Research Organization (KEK) in Tsukuba, Japan, the double-sided silicon strip sub-detector of the Belle II experiment is read out by 1748 APV25 chips. FPGAs perform several calculations on the digitized signals. One of them will be "Hit Time Finding": the determination of the time and amplitude of the signal peaks of each event in real time using pre-programmed neural networks. This work analyses the possibility, precision and reliability of these calculations depending on various parameters.| File | Dimensione | Formato | |
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