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• W93438566 abstract "In this paper, we consider the problem of implementation of neural network in the context of the level 2 trigger of HESS2 project. We propose a hardware architecture which which takes advantage of high parallelism, pipelining and the intrinsic nature of FPGAs. Neural networks have successfully been implemented in a wide variety of appli- cations and keep on demonstrating their efficiency compared to other classical techniques in pattern recognition and classification. One interesting field in which neural techniques have been of a great interest is the triggering of par- ticles in physics experiments (1),(2) . The triggering specificity consists (i) in taking a decision according to collected events and (ii) in identifying the nature of particles issued from an impact within a collider. These experiences have clearly taken advantage of the neural approach in terms of performances and accuracy. In this article, a solution based on a neural system is proposed in a close domain: the gamma-ray astronomy. The presented results have been developed within the HESS collaboration which is interested in detecting cosmic gamma ray sources all around the universe. The HESS1 experiment (3) is based on the atmo- spheric Cherenkov technique: as a high energy cosmic ray hits the atmosphere, it creates an extensive air shower by interaction with the atmosphere. This phe- nomenon is known as Cherenkov light. The experiment consists in studying this Cherenkov light in order to detect the gamma particles hitting the atmosphere. It thus permits to evaluate the position and nature of the gamma-ray sources. The current HESS1 system (phase 1) is composed of four imaging Cherenkov telescopes, arranged on a square. By combining the information of, at least, two telescopes at the same time, the system makes intensive use of the stereoscopic approach. This enables to significantly increase the performances in terms of high energy particle detection (from 100GeV to 50TeV). One of the main specificities of these experiences resides in its capability of processing huge amount of data in a restricted time window. A trigger system composed of two levels has been designed within the HESS1 experiment: a level 1 (L1) per telescope and a unique central trigger. Such a trigger system is useful to make an on-line selection of relevant events for further off-line processing." @default.
• W93438566 created "2016-06-24" @default.
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• W93438566 date "2008-01-01" @default.
• W93438566 modified "2023-10-04" @default.
• W93438566 title "Neural Network hardware architecture for pattern recognition in the HESS2 project" @default.
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