Dynamic perceptron: some theorems about the possibility of parallel pattern recognition with an application to high-energy physics

1994 ◽  
Author(s):  
Antonio L. Perrone ◽  
Patrizia Castiglione ◽  
Gianfranco Basti ◽  
Roberto Messi
Keyword(s):  
Pattern Recognition ◽  
High Energy Physics ◽  
High Energy ◽  
Parallel Pattern ◽  
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.

scholarly journals Quantum machine learning and its supremacy in high energy physics

2020 ◽  
pp. 2030024
Author(s):  
Kapil K. Sharma
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Particle Physics ◽  
High Energy Physics ◽  
Recognition Task ◽  
High Energy ◽  
Particle Identification ◽  
Quantum Machine Learning ◽  
The Future ◽  
Energy Physics

This paper reveals the future prospects of quantum algorithms in high energy physics (HEP). Particle identification, knowing their properties and characteristics is a challenging problem in experimental HEP. The key technique to solve these problems is pattern recognition, which is an important application of machine learning and unconditionally used for HEP problems. To execute pattern recognition task for track and vertex reconstruction, the particle physics community vastly use statistical machine learning methods. These methods vary from detector-to-detector geometry and magnetic field used in the experiment. Here, in this paper, we deliver the future possibilities for the lucid application of quantum computation and quantum machine learning in HEP, rather than focusing on deep mathematical structures of techniques arising in this domain.

scholarly journals Quantum pattern recognition algorithms for charged particle tracking

2021 ◽  
Vol 380 (2216) ◽  
Author(s):  
H. M. Gray
Keyword(s):  
Pattern Recognition ◽  
Charged Particle ◽  
Particle Physics ◽  
High Energy Physics ◽  
Rapid Development ◽  
Hadron Collider ◽  
Simulated Data ◽  
High Energy ◽  
Recognition Algorithms ◽  
Energy Physics

High-energy physics is facing a daunting computing challenge with the large datasets expected from the upcoming High-Luminosity Large Hadron Collider in the next decade and even more so at future colliders. A key challenge in the reconstruction of events of simulated data and collision data is the pattern recognition algorithms used to determine the trajectories of charged particles. The field of quantum computing shows promise for transformative capabilities and is going through a cycle of rapid development and hence might provide a solution to this challenge. This article reviews current studies of quantum computers for charged particle pattern recognition in high-energy physics. This article is part of the theme issue ‘Quantum technologies in particle physics’.

scholarly journals NEURAL NETWORKS IN EXPERIMENTAL HIGH-ENERGY PHYSICS

1992 ◽  
Vol 03 (04) ◽  
pp. 733-771 ◽  
Author(s):  
C. BORTOLOTTO ◽  
A. DE ANGELIS ◽  
N. DE GROOT ◽  
J. SEIXAS
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Pattern Classification ◽  
High Energy Physics ◽  
High Energy ◽  
Classification Problems ◽  
The Status ◽  
Artificial Neural ◽  
Energy Physics

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.

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