Feasibility of using neural networks as a level 2 calorimeter trigger for jet tagging

Conference · Thu Dec 31 00:00:00 EST 1992

OSTI ID:10103989

Handler, T; Neis, E ^[1]; Glover, C; Gabriel, T; Saini, S ^[2]

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1. Tennessee Univ., Knoxville, TN (United States). Dept. of Physics
2. Oak Ridge National Lab., TN (United States)

Several of expected decay modes of the Higgs particle will result in jet formation. We propose to incorporate a second level trigger into the SDC detector, using neural network VSLI hardware, to tag such Higgs decay modes. The input to the neural network will be the energy depositions in both the barrel and endcap regions of the calorimeter. The neural network`s output would be a value representing the degree of correlation between the observed energy distribution and the type of physical scattering that has occurred. Preliminary results indicate that neural networks may be of use in tagging jet decays of the Higgs particle.

• Research Organization: Oak Ridge National Lab., TN (United States)
• Sponsoring Organization: USDOE, Washington, DC (United States)
• DOE Contract Number: AC05-84OR21400
• OSTI ID: 10103989
• Report Number(s): CONF-920966-10; ON: DE93003016
• Resource Relation: Conference: 10. conference on computing in high energy physics,Annecy (France),21-25 Sep 1992; Other Information: PBD: [1992]
• Country of Publication: United States
• Language: English

Related Subjects

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
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NEURAL NETWORKS
TRIGGER CIRCUITS
HIGGS BOSONS
PARTICLE DECAY
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HIGH ENERGY PHYSICS INSTRUMENTATION
MATHEMATICS AND COMPUTERS