An online neural network triggering system for the Tile Calorimeter

Author

Damazio, D.O. ; de Seixas, J.M. ; Magacho, P.V.

Author_Institution

Signal Process. Lab, Fed. Univ. of Rio de Janeiro, Brazil

Volume

49

Issue

2

fYear

2002

fDate

4/1/2002 12:00:00 AM

Firstpage

369

Lastpage

376

Abstract

For the hadronic calorimeter of ATLAS, TileCal, neural processing is used to establish an efficient methodology for the online particle identification in beam tests of calorimeter prototypes. Although beam purity is usually very good for a selected particle type, background from wrong-type particles cannot be avoided and is routinely identified in the offline analysis. The proposed neural system is trained online to identify electrons, pions, and muons at different energy levels and it achieves more than 90% efficiency in terms of particle identification. The neural system is being implemented by integrating it to the readout drive (ROD) of the TileCal.

Keywords

data acquisition; high energy physics instrumentation computing; muon detection; neural nets; nuclear electronics; particle calorimetry; position sensitive particle detectors; readout electronics; trigger circuits; ATLAS; TileCal; beam purity; beam tests; hadronic calorimeter; neural processing; online neural network triggering system; particle identification; readout drive; tile calorimeter; Contamination; Detectors; Large Hadron Collider; Neural networks; Particle beams; Performance evaluation; Prototypes;

fLanguage

English

Journal_Title

Nuclear Science, IEEE Transactions on

Publisher

ieee

ISSN

0018-9499

Type

jour

DOI

10.1109/TNS.2002.1003739

Filename

1003739