Title :
Segmented Independent Component Analysis for Online Filtering Using Highly Segmented Detectors
Author :
Filho, Eduardo F Simas ; de Seixas, Jose Manoel ; Caloba, Luiz Pereira
Author_Institution :
Fed. Univ. of Rio de Janeiro, Rio de Janeiro
Abstract :
An online particle discrimination system is proposed for the ATLAS particle detector, which will be placed at one of the collision points of LHC, the next generation particle collider experiment. segmented independent component analysis (SICA) is applied over a highly segmented calorimeter (energy measurement system) in order to cope with the different levels of granularity present at each segment of the detector. A discrimination efficiency of 97% was achieved for a false alarm probability of 4.8%.
Keywords :
independent component analysis; particle detectors; ATLAS particle detector; Online filtering; Online particle discrimination system; energy measurement system; highly segmented detectors; large hadron collider; segmented independent component analysis; Background noise; Detectors; Electrons; Energy measurement; Event detection; Filtering; Independent component analysis; Intelligent systems; Large Hadron Collider; Mesons;
Conference_Titel :
Intelligent Systems Design and Applications, 2007. ISDA 2007. Seventh International Conference on
Conference_Location :
Rio de Janeiro
Print_ISBN :
978-0-7695-2976-9
DOI :
10.1109/ISDA.2007.107
