Gene Expression Programming and Artficial Neural Network Approaches for Event Selection in High Energy Physics

2006 ◽  
Author(s):  
Liliana Teodorescu ◽  
Ivan D Reid
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
High Energy Physics ◽  
Gene Expression Programming ◽  
High Energy ◽  
Event Selection ◽  
Network Approaches ◽  
Energy Physics

Neural network trigger algorithms for heavy quark event selection in a fixed target high energy physics experiment

1992 ◽  
Vol 25 (4) ◽  
pp. 413-421 ◽  
Author(s):  
Lalit Gupta ◽  
Anand M. Upadhye ◽  
Bruce Denby ◽  
Salvator R. Amendolia ◽  
Giovanni Grieco
Keyword(s):  
Neural Network ◽  
Heavy Quark ◽  
High Energy Physics ◽  
High Energy ◽  
Fixed Target ◽  
Event Selection ◽  
Physics Experiment ◽  
Energy Physics

scholarly journals The Use of Neural Networks in High-Energy Physics

1993 ◽  
Vol 5 (4) ◽  
pp. 505-549 ◽  
Author(s):  
Bruce Denby
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
High Energy Physics ◽  
High Energy ◽  
High Quality ◽  
Network Algorithms ◽  
The Past ◽  
The Neural Network ◽  
Energy Physics

In the past few years a wide variety of applications of neural networks to pattern recognition in experimental high-energy physics has appeared. The neural network solutions are in general of high quality, and, in a number of cases, are superior to those obtained using "traditional'' methods. But neural networks are of particular interest in high-energy physics for another reason as well: much of the pattern recognition must be performed online, that is, in a few microseconds or less. The inherent parallelism of neural network algorithms, and the ability to implement them as very fast hardware devices, may make them an ideal technology for this application.

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