a Multiscale, Lacunarity and Neural Network Method for γ/h Discrimination in Extensive Air Showers
Abstract
This paper presents 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. The separation technique is particularly suited for being applied when the topology of the particle distribution in the shower front is as largely detailed as possible. In the present work the method is discussed and applied 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.
- Publication:
-
Science: Image in Action
- Pub Date:
- December 2012
- DOI:
- 10.1142/9789814383295_0025
- Bibcode:
- 2012sia..conf..283P
- Keywords:
-
- Cosmic Rays;
- Extensive Air Showers;
- Multiscale Analysis;
- Wavelet Methods;
- Neural Networks