Flekova, L. ; Schott, M. (2017)
Reconstruction of Micropattern Detector Signals using Convolutional Neural Networks.
In: Journal of Physics: Conference Series, 898 (3)
doi: 10.1088/1742-6596/898/3/032054
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
Micropattern gaseous detector (MPGD) technologies, such as GEMs or MicroMegas, are particularly suitable for precision tracking and triggering in high rate environments. Given their relatively low production costs, MPGDs are an exemplary candidate for the next generation of particle detectors. Having acknowledged these advantages, both the ATLAS and CMS collaborations at the LHC are exploiting these new technologies for their detector upgrade programs in the coming years. When MPGDs are utilized for triggering purposes, the measured signals need to be precisely reconstructed within less than 200 ns, which can be achieved by the usage of FPGAs.
In this work, we present a novel approach to identify reconstructed signals, their timing and the corresponding spatial position on the detector. In particular, we study the effect of noise and dead readout strips on the reconstruction performance. Our approach leverages the potential of convolutional neural network (CNNs), which have recently manifested an outstanding performance in a range of modeling tasks. The proposed neural network architecture of our CNN is designed simply enough, so that it can be modeled directly by an FPGA and thus provide precise information on reconstructed signals already in trigger level.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Flekova, L. ; Schott, M. |
| Art des Eintrags: | Bibliographie |
| Titel: | Reconstruction of Micropattern Detector Signals using Convolutional Neural Networks |
| Sprache: | Englisch |
| Publikationsjahr: | 2017 |
| Ort: | Bristol |
| Verlag: | IOP Publishing |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Journal of Physics: Conference Series |
| Jahrgang/Volume einer Zeitschrift: | 898 |
| (Heft-)Nummer: | 3 |
| Kollation: | 6 Seiten |
| DOI: | 10.1088/1742-6596/898/3/032054 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Micropattern gaseous detector (MPGD) technologies, such as GEMs or MicroMegas, are particularly suitable for precision tracking and triggering in high rate environments. Given their relatively low production costs, MPGDs are an exemplary candidate for the next generation of particle detectors. Having acknowledged these advantages, both the ATLAS and CMS collaborations at the LHC are exploiting these new technologies for their detector upgrade programs in the coming years. When MPGDs are utilized for triggering purposes, the measured signals need to be precisely reconstructed within less than 200 ns, which can be achieved by the usage of FPGAs. In this work, we present a novel approach to identify reconstructed signals, their timing and the corresponding spatial position on the detector. In particular, we study the effect of noise and dead readout strips on the reconstruction performance. Our approach leverages the potential of convolutional neural network (CNNs), which have recently manifested an outstanding performance in a range of modeling tasks. The proposed neural network architecture of our CNN is designed simply enough, so that it can be modeled directly by an FPGA and thus provide precise information on reconstructed signals already in trigger level. |
| ID-Nummer: | Artikel-ID: 032054 |
| Zusätzliche Informationen: | Track 1: Online Computing |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik 500 Naturwissenschaften und Mathematik > 530 Physik |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung |
| Hinterlegungsdatum: | 17 Mai 2024 09:38 |
| Letzte Änderung: | 17 Mai 2024 09:38 |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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Reconstruction of Micropattern Detector Signals using Convolutional Neural Networks. (deposited 14 Mai 2024 09:52)
- Reconstruction of Micropattern Detector Signals using Convolutional Neural Networks. (deposited 17 Mai 2024 09:38) [Gegenwärtig angezeigt]
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