Neural second-level trigger system based on calorimetry

• Published in: Computer physics communications Vol. 95; no. 2; pp. 143 - 157
• Main Authors: Seixas, J.M., Caloba, L.P., Souza, M.N., Braga, A.L., Rodrigues, A.P.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 1996; Elsevier Science
• Subjects: Calorimeters; Computer data analysis; Cyclic accelerators and storage rings; Exact sciences and technology; Experimental methods and instrumentation for elementary-particle and nuclear physics; Nuclear physics; Physics; Radiation detectors, dosimeters; Storage rings; Monte Carlo method; LHC collider; Neural networks; Instrumentation; Calorimetry; Jets; Computerized simulation; Triggering; Quantum chromodynamics
• ISSN: 0010-4655; 1879-2944
• DOI: 10.1016/0010-4655(96)00012-4

A second-level triggering system based on calorimetry is analyzed using neural networks. Calorimeter data in a LHC environment is obtained with Monte Carlo simulations and an algorithm for the first-level trigger operation is applied. The surviving events are then available as a 20×20 matrix information corresponding to the calorimeter towers in the region of interest. The dominant background for triggering on electrons is assumed to consist of QCD jets which passed the first-level trigger condition.
The main features of the calorimeter are extracted. Matrix information, shower deposition in concentric rings and tail weighting procedures are studied. The processed information is sent to a fully connected backpropagation neural network. In this analysis we also consider pileup effects of an average of 20 minimum bias events. The neural network based system achieved up to 99% electron efficiency with less than 9% of jets being misclassified as electrons. Implementation on digital signal processors is suggested.