Online particle detection with neural networks based on topological calorimetry information - INSPIRE

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Online particle detection with neural networks based on topological calorimetry information

T. Ciodaro
(
- Rio de Janeiro Federal U.
)
,
D. Deva
(
- Rio de Janeiro Federal U.
)
,
J.M. de Seixas
(
- Rio de Janeiro Federal U.
)
,
D. Damazio
(
- Brookhaven
)

2012

11 pages

Part of Proceedings, 14th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2011) : Uxbridge, UK, September 5-9, 2011

Published in:

J.Phys.Conf.Ser. 368 (2012) 012030

Contribution to:

- ACAT 2011

DOI:

10.1088/1742-6596/368/1/012030

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Abstract: (IOP)

This paper presents the latest results from the Ringer algorithm, which is based on artificial neural networks for the electron identification at the online filtering system of the ATLAS particle detector, in the context of the LHC experiment at CERN. The algorithm performs topological feature extraction using the ATLAS calorimetry information (energy measurements). The extracted information is presented to a neural network classifier. Studies showed that the Ringer algorithm achieves high detection efficiency, while keeping the false alarm rate low. Optimizations, guided by detailed analysis, reduced the algorithm execution time by 59%. Also, the total memory necessary to store the Ringer algorithm information represents less than 6.2 percent of the total filtering system amount.

neural network
calorimeter
topological
ATLAS
efficiency
data analysis method
numerical methods
performance

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