Neural second-level trigger system based on calorimetry

✍ Scribed by J.M. Seixas; L.P. Caloba; M.N. Souza; A.L. Braga; A.P. Rodrigues

• Publisher: Elsevier Science
• Year: 1996
• Tongue: English
• Weight: 902 KB
• Volume: 95
• Category: Article
• ISSN: 0010-4655
• DOI: 10.1016/0010-4655(96)00012-4

✦ Synopsis

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 20x20 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 ~ignal processors is suggested.