Pattern Recognition in High Energy Physics with Neural Networks

1992 ◽  
pp. 149-163 ◽  
Author(s):  
Carsten Peterson
1993 ◽  
Vol 5 (4) ◽  
pp. 505-549 ◽  
Author(s):  
Bruce Denby

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.


1992 ◽  
Vol 03 (04) ◽  
pp. 733-771 ◽  
Author(s):  
C. BORTOLOTTO ◽  
A. DE ANGELIS ◽  
N. DE GROOT ◽  
J. SEIXAS

During the last years, the possibility to use Artificial Neural Networks in experimental High Energy Physics has been widely studied. In particular, applications to pattern recognition and pattern classification problems have been investigated. The purpose of this article is to review the status of such investigations and the techniques established.


2016 ◽  
Vol 93 (9) ◽  
Author(s):  
Pierre Baldi ◽  
Kevin Bauer ◽  
Clara Eng ◽  
Peter Sadowski ◽  
Daniel Whiteson

2019 ◽  
Vol 214 ◽  
pp. 06027
Author(s):  
Adrian Bevan ◽  
Thomas Charman ◽  
Jonathan Hays

HIPSTER (Heavily Ionising Particle Standard Toolkit for Event Recognition) is an open source Python package designed to facilitate the use of TensorFlow in a high energy physics analysis context. The core functionality of the software is presented, with images from the MoEDAL experiment Nuclear Track Detectors (NTDs) serving as an example dataset. Convolutional neural networks are selected as the classification algorithm for this dataset and the process of training a variety of models with different hyper-parameters is detailed. Next the results are shown for the MoEDAL problem demonstrating the rich information output by HIPSTER that enables the user to probe the performance of their model in detail.


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