Improving Air Shower Simulations by Tuning Pythia 8/Angantyr with Accelerator Data

TL;DR

This paper improves air shower simulations by tuning the Pythia 8/Angantyr event generator using collider and fixed-target experimental data, enhancing the accuracy of particle density and energy deposit predictions.

Contribution

It introduces a combined tuning approach for Pythia 8/Angantyr using advanced statistical methods, optimizing parameters for cosmic ray air shower simulations.

Findings

Enhanced simulation accuracy for particle densities at ground level

Improved energy deposit profile predictions

Quantified uncertainties in air shower observables

Abstract

We present a combined analysis of the Pythia 8 event generator using accelerator data and evaluate its impact on air shower observables. Reliable simulations with event generators are essential for particle physics analyses, achievable through advanced tuning to experimental data. Pythia 8 has emerged as a promising high-energy interaction model for cosmic ray air shower simulations, offering well-documented parameter settings and a user-friendly interface to enable automatic tuning efforts. Using data from collider and fixed-target experiments, we first derive tunes for each domain separately, before tuning both domains simultaneously. To achieve this, we define a core set of observables and quantify their dependence on selected parameters. The tuning efforts are based on gradient descent and Bayesian methods, the latter providing a full uncertainty propagation of the parameters to the observables. Results for the impact of a combined analysis for the Pythia 8/Angantyr event generator on air shower observables, such as particle densities at ground level and energy deposit profiles, are presented.