Real time track identification with artificial neural networks

Real time track identification with artificial neural networks

An artificial neural network (ANN) with local connectivities is proposed as a track identifier for high energy physics experiments. Realistic constraints from its possible hardware implementation as a first level trigger are taken into consideration. The performance of the ANN was evaluated with dat...

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Veröffentlicht in:Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment Accelerators, spectrometers, detectors and associated equipment, 1993-01, Vol.324 (1), p.320-329
Hauptverfasser: Athanasiu, G., Pavlopoulos, P., Vlachos, S.
Format: Artikel
Sprache:eng
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Zusammenfassung:An artificial neural network (ANN) with local connectivities is proposed as a track identifier for high energy physics experiments. Realistic constraints from its possible hardware implementation as a first level trigger are taken into consideration. The performance of the ANN was evaluated with data of the CP-LEAR experiment, with very encouraging results. Such a ANN as a neural first level trigger (NFLT) may be relevent for LHC, SSC, HERA experiments.
ISSN:0168-9002
1872-9576
DOI:10.1016/0168-9002(93)90995-T