Efficient Modeling of Heavy Cosmic Rays Propagation in Evolving Astrophysical Environments

TL;DR

This paper introduces a versatile energy transport code for modeling the complex, non-linear evolution of cosmic-ray spectra, including heavy elements, within astrophysical environments, improving predictions of observables like neutrino fluxes.

Contribution

The novel software enables detailed, non-linear modeling of cosmic-ray propagation with heavy nuclei, incorporating feedback mechanisms and modular interaction models for astrophysical applications.

Findings

Accurately models heavy cosmic-ray nuclei propagation.

Predicts neutrino emissions based on chemical composition.

Enhances interpretation of astrophysical observations.

Abstract

We present a new energy transport code that models the time dependent and non-linear evolution of spectra of cosmic-ray nuclei, their secondaries, and photon target fields. The software can inject an arbitrary chemical composition including heavy elements up to iron nuclei. Energy losses and secondary production due to interactions of cosmic ray nuclei, secondary mesons, leptons, or gamma-rays with a target photon field are available for all relevant processes, e.g., photo-meson production, photo disintegration, synchrotron radiation, Inverse Compton scattering, and more. The resulting x-ray fluxes can be fed back into the simulation chain to correct the initial photon targets, resulting in a non-linear treatment of the energy transport. The modular structure of the code facilitates simple extension of interaction or target field models. We will show how the software can be used to improve predictions of observables in various astrophysical sources such as jetted active galactic nuclei (AGN). Since the software can model the propagation of heavy ultrahigh-energy cosmic rays inside the source it can precisely predict the chemical composition at the source. This will also refine predictions of neutrino emissions - they strongly depend on the chemical composition. This helps in the future to optimize the selection and analyses of data from the IceCube neutrino observatory with the aim to enhance the sensitivity of IceCube and reduce the number of trial factors.