A discrimination technique for extensive air showers based on multiscale, lacunarity and neural network analysis

Giacomo D'Ali'Staiti, D'Alí Staiti, Fabio D'Anna, Antonio Pagliaro

Risultato della ricerca: Other

Abstract

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.
Lingua originaleEnglish
Pagine286-292
Numero di pagine7
Stato di pubblicazionePublished - 2011

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Nuclear and High Energy Physics

Fingerprint Entra nei temi di ricerca di 'A discrimination technique for extensive air showers based on multiscale, lacunarity and neural network analysis'. Insieme formano una fingerprint unica.

Cita questo