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In particle collider experiments a huge amount of information is produced, but only a small part is relevant for physics characterization. An efficient filtering (trigger) system is required to guarantee that valuable signatures will be recorded and most of the background noise rejected. In previous works the standard linear independent component analysis (ICA) model was used for feature extraction...
A novel particle discrimination strategy is proposed in this work for the ATLAS detector high-level trigger. The available data set, composed by electron and jet signatures, was clustered using self-organizing maps and local independent components were estimated for each group. A hybrid neural-genetic structure was used as classifier. Considered performance improvement was achieved with the proposed...
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...
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