Bursts of extensive air showers: chaos vs. stochasticity

Abstract

Bursts of the count rate of extensive air showers (EAS) lead to the appearance of clusters in time series that represent EAS arrival times. We apply methods of nonlinear time series analysis to twenty EAS cluster events found in the data set obtained with the EAS-1000 prototype array. In particular, we use the Grassberger-Procaccia algorithm to compute the correlation dimension of the time series in the vicinity of the clusters. We find that four cluster events produce signs of chaos in the corresponding time series. By applying a number of supplementary methods we assess that the nature of the observed behaviour of the correlation dimension is likely to be deterministic. We suggest a simple qualitative model that might explain an origin of clusters in general and "possibly chaotic" clusters in particular. Finally, we compare our conclusions with the results of similar investigations performed by the EAS-TOP and LAAS groups.