scholarly journals Deep Neural Network resizing for real-time applications in High Energy Physics

2021 ◽  
Author(s):  
Andrea Di Luca ◽  
Daniela Mascione ◽  
Francesco Maria Follega ◽  
Marco Cristoforetti ◽  
Roberto Iuppa
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Deep Neural Network ◽  
High Energy Physics ◽  
High Energy ◽  
Real Time Applications ◽  
Energy Physics

Neural network for real-time particle discrimination in high-energy physics

1994 ◽  
Author(s):  
Roberto Messi ◽  
Enrico Pasqualucci ◽  
Luciano Paoluzi ◽  
Antonio L. Perrone ◽  
Gianfranco Basti
Keyword(s):  
Neural Network ◽  
Real Time ◽  
High Energy Physics ◽  
High Energy ◽  
Particle Discrimination ◽  
Energy Physics

A Neural Network for Real-Time Particle Discrimination in High-Energy Physics

1996 ◽  
Vol 3 (2) ◽  
pp. 90-107
Author(s):  
Antonio L. Perrone ◽  
Enrico Pasqualucci ◽  
Roberto Messi ◽  
Gianfranco Basti ◽  
Luciano Paoluzi
Keyword(s):  
Neural Network ◽  
Real Time ◽  
High Energy Physics ◽  
High Energy ◽  
Particle Discrimination ◽  
Energy Physics

scholarly journals DRIFT CHAMBER TRACKING WITH A VLSI NEURAL NETWORK

1992 ◽  
Vol 03 (supp01) ◽  
pp. 285-295
Author(s):  
Clark S. Lindsey ◽  
Bruce Denby ◽  
Herman Haggerty ◽  
Ken Johns
Keyword(s):  
Neural Network ◽  
Real Time ◽  
High Energy Physics ◽  
Charged Particles ◽  
Drift Chamber ◽  
High Energy ◽  
Analog Vlsi ◽  
Test Setup ◽  
Energy Physics

We have tested a commercial analog VLSI neural network chip for finding in real time the intercept and slope of charged particles traversing a drift chamber. Voltages proportional to the drift times were input to the Intel ETANN chip and the outputs were recorded and later compared off line to conventional track fits. We will discuss the chamber and test setup, the chip specifications, and results of recent tests. We’ll briefly discuss possible applications in high energy physics detector triggers.

IMAGE RECOVERY OF TRANSIENT VOLTAGE BASED ON REAL-TIME MONITORING

2008 ◽  
Vol 22 (05) ◽  
pp. 353-358 ◽  
Author(s):  
YING LIU ◽  
CHI XIE
Keyword(s):  
Electromagnetic Field ◽  
Real Time ◽  
High Energy Physics ◽  
High Energy ◽  
Image Recovery ◽  
Real Time Monitoring ◽  
Electromagnetic Coils ◽  
Transient Voltage ◽  
Transient Overvoltage ◽  
Energy Physics

In modern high-energy physics, a powerful electromagnetic field must be supplied for some elementary particles to be accelerated by passing through the region of high-energy physics fields. The electric current and high voltage producing the powerful electromagnetic field are very important to high-energy accelerators, but the insulation of electromagnetic coils in the accelerators suffers from electric damage under powerful electricity. Epecially, it may be stricken by transient overvoltage from the a.c. generator or electric network at any time. For the insulation problem of electromagnetic coils in the accelerator stricken by transient overvoltage, based on real-time monitoring and virtual image technique, the image recovery of transient voltage and the insulation safety of electromagnetic coils in the accelerator can be analyzed and predicted on-line.

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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