USING AN ANALOG NEURAL NETWORK TO TRIGGER ON TAU LEPTONS AT CDF

J.S. Conway and C. Loomis
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J.S. Conway: Department of Physics and Astronomy Rutgers, The State University of New Jersey Piscataway, New Jersey 08855, USA
C. Loomis: Department of Physics and Astronomy Rutgers, The State University of New Jersey Piscataway, New Jersey 08855, USA

International Journal of Modern Physics C (IJMPC), 1995, vol. 06, issue 04, 549-554

Abstract: At the Collider Detector at Fermilab (CDF), we have designed and implemented a trigger for tau leptons using analog neural network electronics. Tau leptons offer a fertile area of research both for standard model tests and for new physics searches. Because the bulk of tau leptons decay into hadrons, it is challenging to distinguish them from ordinary hadron jets. Neural networks are well suited to this type of difficult classification problem. In this case, software simulations show that an efficiency of 15% with a rejection factor of 100 could be obtained. The input to the network is a5×5×2array of calorimeter tower energies surrounding the seed tower of a cluster. If the network’s single output exceeds a tunable threshold, the event is passed to the next stage of the trigger. An existing system based on the Intel ETANN (Electrically Trainable Analog Neural Network) chip was used to implement the tau lepton neural network trigger. The performance of the trigger in current CDF data will be presented.

Date: 1995
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DOI: 10.1142/S0129183195000411

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