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Drift chamber tracking with neural networks

Conference · · IEEE Transactions on Nuclear Science (Institute of Electrical and Electronics Engineers); (United States)
OSTI ID:5922825
; ;  [1]
  1. Fermi National Accelerator Lab., Batavia, IL (United States)
With the very high event rates projected for experiments at the SSC and LHC, it is important to investigate new approaches to on line pattern recognition. The use of neural networks for pattern recognition. The use of neural networks for pattern recognition in high energy physics detectors has been an area of very active research. The authors discuss drift chamber tracking with a commercial analog VLSI neural network chip. Voltages proportional to the drift times in a 4-layer drift chamber were presented to the Intel ETANN chip. The network was trained to provide the intercept and slope of straight tracks traversing the chamber. The outputs were recorded and later compared off line to conventional track fits. Two types of network architectures were studied. Applications of neural network tracking to high energy physics detector triggers is discussed.
OSTI ID:
5922825
Report Number(s):
CONF-921005--
Conference Information:
Journal Name: IEEE Transactions on Nuclear Science (Institute of Electrical and Electronics Engineers); (United States) Journal Volume: 40:4 part 1
Country of Publication:
United States
Language:
English