The Use of Neural Networks in High-Energy Physics
Использование нейронных сетей в физике высоких энергий
1993-07-01
SCID: 54.1/k3nwnpg3
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hardware implementationhigh-energy physicsneural networkspattern recognitionreal-time/online processing
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Abstract (AI)
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.
Key Findings
1
Neural network solutions generally achieve high quality and in several cases outperform traditional methods.
2
Neural networks are particularly valuable for online pattern recognition requiring latencies of a few microseconds or less.
3
Neural networks have been widely applied to pattern recognition tasks in experimental high-energy physics in recent years.
4
The inherent parallelism of neural network algorithms and their implementability as fast hardware devices make them well-suited for low-latency high-energy physics applications.
Research Object
Neural networks applied to pattern recognition in experimental high-energy physics
Research Subject
Their use for online (microsecond-scale) pattern recognition performance and suitability (including inherent parallelism and hardware implementation potential) compared to traditional methods
Publication Details
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1993-07-01
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