Modified Hadronic Interactions and the future of UHECR observations

TL;DR

This paper develops a library of modified hadronic interaction models within existing constraints to better understand ultra-high-energy cosmic ray air showers, aiming to improve model predictions and guide future observatory designs.

Contribution

It introduces a comprehensive library of UHECR simulations with varied hadronic interactions, within accelerator constraints, to assess uncertainties and improve modeling accuracy.

Findings

Certain modifications align with Pierre Auger Observatory data on muon content and shower depth.

A realistic range of hadronic interaction variations is established for UHECR modeling.

The library aids in quantifying uncertainties for future observatory performance predictions.

Abstract

Data from multiple experiments suggest that the current interaction models used in Monte Carlo simulations do not correctly reproduce the hadronic interactions in air showers produced by ultra-high-energy cosmic rays (UHECR). We have created a large library of UHECR simulations where the interactions at the highest energies are slightly modified in various ways - but always within the constraints of the accelerator data, without any abrupt changes with energy and without assuming any specific mechanism or dramatically new physics at the ultra-high energies. Recent results of the Pierre Auger Observatory indicate a need for a change in the prediction of the models for both the muon content at ground and the depth of the maximum of longitudinal development of the shower. In our parameter space, we find combinations of modifications that are in agreement with this analysis, however a consistent description of UHECR showers remains elusive. Our library however provides a realistic representation of the freedom in the modeling of the hadronic interactions and offers an opportunity to quantify uncertainties of various predictions. This can be particularly valuable for the design of future observatories where hadronic models are often used as input for the prediction of the performance. We demonstrate this powerful capability on several selected examples.