Comparison of hybrid and pure Monte Carlo shower generators on an event by event basis

TL;DR

This paper compares hybrid SENECA and pure CORSIKA Monte Carlo air shower simulations on an event-by-event basis, demonstrating that SENECA achieves similar accuracy with significantly reduced computation time.

Contribution

It introduces a method to compare hybrid and pure Monte Carlo simulations on individual showers, validating SENECA's accuracy and efficiency against CORSIKA.

Findings

SENECA agrees with CORSIKA in particle densities, energies, and timing.

SENECA is approximately 10 times faster than CORSIKA for 10 EeV proton showers.

SENECA produces indistinguishable ground particle distributions compared to CORSIKA.

Abstract

SENECA is a hybrid air shower simulation written by H. Drescher that utilizes both Monte Carlo simulation and cascade equations. By using the cascade equations only in the high energy portion of the shower, where the shower is inherently one-dimensional, SENECA is able to utilize the advantages in speed from the cascade equations yet still produce complete, three dimensional particle distributions at ground level which capture the shower to shower variations coming from the early interactions. We present a comparison, on an event by event basis, of SENECA and CORSIKA, a well trusted MC simulation code. By using the same first interaction in both SENECA and CORSIKA, the effect of the cascade equations can be studied within a single shower, rather than averaged over many showers. Our study shows that for showers produced in this manner, SENECA agrees with CORSIKA to a very high accuracy with respect to densities, energies, and timing information for individual species of ground-level particles from both iron and proton primaries with energies between 1 EeV and 100 EeV. Used properly, SENECA produces ground particle distributions virtually indistinguishable from those of CORSIKA in a fraction of the time. For example, for a shower induced by a 10 EeV proton, SENECA is 10 times faster than CORSIKA, with comparable accuracy.