6533b826fe1ef96bd128487d
RESEARCH PRODUCT
Neural Classification of HEP Experimental Data
S.m. SiniscalchiSalvatore VitabileGiorgio VassalloAntonio GentileFilippo SorbelloGiovanni Pilatosubject
Artificial neural networkComputer engineeringComputer scienceExperimental dataNeural Networks Intelligent Data Analysis Embedded Neural NetworksArchitecturePerceptronNetwork topologydescription
High Energy Physics (HEP) experiments require discrimination of a few interesting events among a huge number of background events generated during an experiment. Hierarchical triggering hardware architectures are needed to perform this tasks in real-time. In this paper three neural network models are studied as possible candidate for such systems. A modified Multi-Layer Perception (MLP) architecture and a E alpha Net architecture are compared against a traditional MLP Test error below 25% is archived by all architectures in two different simulation strategies. E alpha Net performance are 1 to 2% better on test error with respect to the other two architectures using the smaller network topology. The design of a digital implementation of the proposed neural network is also outlined.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2009-03-06 |