Neural network chips for trigger purposes in high energy physics

2002 ◽  
Author(s):  
H. Gemmeke ◽  
W. Eppler ◽  
T. Fischer ◽  
A. Menchikov ◽  
S. Neusser
Keyword(s):  
Neural Network ◽  
High Energy Physics ◽  
High Energy ◽  
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.

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

A NEURAL NETWORK CLASSIFIER IN EXPERIMENTAL PARTICLE PHYSICS

1993 ◽  
Vol 04 (02) ◽  
pp. 95-108 ◽  
Author(s):  
AMIR A. HANDZEL ◽  
T. GROSSMAN ◽  
E. DOMANY ◽  
S. TAREM ◽  
E. DUCHOVNI
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Particle Physics ◽  
High Energy Physics ◽  
Simulated Data ◽  
Classification Problem ◽  
High Energy ◽  
Neural Network Classifier ◽  
Backpropagation Algorithm ◽  
Energy Physics

A classification problem in high energy physics has been solved on simulated data using a simple multilayer perceptron comprising binary units which was trained with the CHIR algorithm. The unstable training of such a network on a nonseparable set has been overcome by selecting those weight vectors with good performance while providing a flexible choice of the two types of classification errors. Specific features of the problem have been exploited in order to simplify and optimize the solution which has been compared to the popular backpropagation algorithm and found to perform on a similar level. Additional aspects of this work are the use of the CHIR algorithm on continuous input and incorporating the classic idea of a Φ-machine in a multilayer perceptron.

scholarly journals Adversarial domain adaptation to reduce sample bias of a high energy physics classifier

2021 ◽  
Author(s):  
José Manuel Manuel Clavijo Columbié ◽  
Paul Glaysher ◽  
Jenia Jitsev ◽  
Judith Maria Katzy
Keyword(s):  
Neural Network ◽  
High Energy Physics ◽  
Domain Adaptation ◽  
Learning Algorithm ◽  
High Energy ◽  
Machine Learning Algorithm ◽  
Sample Bias ◽  
The Difference ◽  
Background Events ◽  
Energy Physics

Abstract We apply adversarial domain adaptation to reduce sample bias in a classification machine learning algorithm. We add a gradient reversal layer to a neural network to simultaneously classify signal versus background events, while minimising the difference of the classifier response to a background sample using an alternative MC model. We show this on the example of simulated events at the LHC with $t\bar{t}H$ signal versus $t\bar{t}b\bar{b}$ background classification.

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