Szczegóły publikacji
Opis bibliograficzny
Using deep neural networks to improve the precision of fast-sampled particle timing detectors / Mateusz Kocot, Krzysztof Misan, Valentina AVATI, Edoardo Bossini, Leszek GRZANKA, Nicola Minafra // Computer Science ; ISSN 1508-2806. — 2024 — vol. 25 no. 1, s. 43–61. — Bibliogr. s. 59–61, Abstr. — Publikacja dostępna online od: 2024-03-10
Autorzy (6)
- AGHKocot Mateusz
- AGHMisan Krzysztof
- AGHAvati Valentina
- Bossini Edoardo
- AGHGrzanka Leszek
- Minafra Nicola
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 156191 |
|---|---|
| Data dodania do BaDAP | 2024-11-20 |
| Tekst źródłowy | URL |
| DOI | 10.7494/csci.2024.25.1.5784 |
| Rok publikacji | 2024 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Computer Science |
Abstract
Measurements from particle timing detectors are often affected by the time walk effect caused by statistical fluctuations in the charge deposited by passing particles. The constant fraction discriminator (CFD) algorithm is frequently used to mitigate this effect both in test setups and in running experiments, such as the CMS-PPS system at the CERN’s LHC. The CFD is simple and effective but does not leverage all voltage samples in a time series. Its performance could be enhanced with deep neural networks, which are commonly used for time series analysis, including computing the particle arrival time. We evaluated various neural network architectures using data acquired at the test beam facility in the DESY-II synchrotron, where a precise MCP (MicroChannel Plate) detector was installed in addition to PPS diamond timing detectors. MCP measurements were used as a reference to train the networks and compare the results with the standard CFD method. Ultimately, we improved the timing precision by 8% to 23%, depending on the detector’s readout channel. The best results were obtained using a UNet-based model, which outperformed classical convolutional networks and the multilayer perceptron.
