# Road map for the tuning of hadronic interaction models with accelerator-based and astroparticle data

## TL;DR

This paper presents a comprehensive roadmap for tuning hadronic interaction models by integrating data from both accelerator-based experiments and astroparticle observations, enhancing the accuracy of event generators in high-energy physics.

## Contribution

It introduces a novel unified tuning approach for hadronic interaction models using combined accelerator and astroparticle data sets.

## Key findings

- Unified tuning framework for event generators.
- Improved modeling of hadronic collisions at high energies.
- Enhanced agreement with experimental data across datasets.

## Abstract

In high-energy and astroparticle physics, event generators play an essential role, even in the simplest data analyses. As analysis techniques become more sophisticated, e.g. based on deep neural networks, their correct description of the observed event characteristics becomes even more important. Physical processes occurring in hadronic collisions are simulated within a Monte Carlo framework. A major challenge is the modeling of hadron dynamics at low momentum transfer, which includes the initial and final phases of every hadronic collision. QCD-inspired phenomenological models used for these phases cannot guarantee completeness or correctness over the full phase space. These models usually include parameters which must be tuned to suitable experimental data. Until now, event generators have been developed and tuned mainly on the basis of data from high-energy physics experiments at accelerators. The wealth of data available from the latest generation of astroparticle experiments has not yet been fully exploited, and in many cases is not satisfactorily described. Both kinds of data sets are complementary as astroparticle experiments provide sensitivity especially to hadrons produced nearly parallel to the collision axis and cover center-of-mass energies up to several hundred TeV, well beyond those reached at colliders so far. In this report, we provide an overview of state-of-the-art event generators and their tuning, including the most relevant inputs from high-energy accelerator and astroparticle experiments. We present a road map that shows, for the first time, how the unified tuning of event generators with accelerator-based and astroparticle data can be performed.