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A hybrid training method for neural energy estimation in calorimetry
A neural mapping is developed to improve the overall performance of Tilecal, which is the hadronic calorimeter of the ATLAS detector. Feeding the input nodes of a multilayer feedforward neural network with the energy values sampled by the calorimeter cells in beam tests, it is shown that the origina...
| Autores principales: | , , |
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| Lenguaje: | eng |
| Publicado: |
2001
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| Materias: | |
| Acceso en línea: | http://cds.cern.ch/record/536829 |