A multiscale method for gamma/h discrimination in extensive air showers

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. 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. Here, our method is discussed and applied to a set of fully simulated vertical showers in the experimental framework of ARGO-YBJ, taking advantage of both the space and time distribution of the detected secondary particles in the shower front, to obtain hadron to gamma primary separation in EAS analysis. We show that the presented approach gives very good results, leading, in the 1-10 Tev energy range, to an improvement of the discrimination power with respect to the existing figures for extended shower detectors. The technique shows up to be very promising and its application may have important astrophysical prospects in different experimental environment of extended air shower study.