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I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .
Risultati sfruttabili
The project is intended to study probabilistic models and to develop new methods of statistical analysis of experimental data obtained from modern electronic experiments conducted on the big new accelerators of charged particles in high energy physics. The characteristics of these experiments are: the extreme rates and occupancies of data taking by such detectors as proportional and electromagnetic calorimeters; the high level of uncertainties in detectors like High Pressure Drift Tubes (HPDT); and the background level exceeding useful signals by many orders of magnitude and the growing event multiplicity. That leads to the inapplicability of conventional data handling methods. To elaborate new ones the project will apply the following innovations: artificial neural network (ANN) modifications to be used for particle track recognition as well as for physical parameter extracting; use of a robust method for reliable curve fitting in cases of contaminated data and for providing the repulsive force in the ANN evaluation procedure. On the basis of these methods new fast and accurate algorithms will be developed with the possibility of their parallel hardware implementation.
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