6533b861fe1ef96bd12c466d
RESEARCH PRODUCT
A discrimination technique for extensive air showers based on multiscale, lacunarity and neural network analysis
G. Dʼalí StaitiG. Dʼalí StaitiAntonio PagliaroF. Dʼannasubject
PhysicsWavelet MethodNuclear and High Energy PhysicsNeural NetworksArtificial neural networkAstrophysics::High Energy Astrophysical PhenomenaCosmic Rays; Extensive Air Showers; Multiscale Analysis; Wavelet Methods; Neural NetworksMultiscale AnalysiDetectorSettore FIS/01 - Fisica SperimentaleExtensive Air ShowerCosmic rayMultifractal systemCosmic RayAtomic and Molecular Physics and OpticsSet (abstract data type)LacunarityRange (statistics)High Energy Physics::ExperimentAlgorithmEnergy (signal processing)Simulationdescription
We present a new method for the identification of extensive air showers initiated by different primaries. The method uses the multiscale concept and is based on the analysis of multifractal behaviour and lacunarity of secondary particle distributions together with a properly designed and trained artificial neural network. In the present work the method is discussed and applied to a set of fully simulated vertical showers, in the experimental framework of ARGO-YBJ, to obtain hadron to gamma primary separation. We show that the presented approach gives very good results, leading, in the 1–10 TeV energy range, to a clear improvement of the discrimination power with respect to the existing figures for extended shower detectors.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2011-03-01 |