In a test setup, a hardware neural network determined track parameters of charged particles traversing a drift chamber. Voltages proportional to the drift times in 6 cells of the 3-layer chamber were inputs to the Intel ETANN neural network chip which had been trained to give the slope and intercept of tracks. We compare network track parameters to those obtained from off-line track fits. To our knowledge this is the first on-line application of a VLSI neural network to a high energy physics detector. This test explored the potential of the chip and the practical problems of using it in a real world setting. We compare the chip performance to a neural network simulation on a conventional computer. We discuss possible applications of the chip in high energy physics detector triggers.
ASJC Scopus subject areas
- Nuclear and High Energy Physics