Universal properties of primary and secondary cosmic ray energy spectra

Abstract

Atomic nuclei appearing in cosmic rays are typically classified as primary or secondary. However, a better understanding of their origin and propagation properties is still necessary. We analyse the flux of primary (He, C, O) and secondary nuclei (Li, Be, B) detected with rigidity (momentum/charge) between 2 GV and 3 TV by the Alpha Magnetic Spectrometer (AMS) on the International Space Station. We show that $q$-exponential distribution functions, as motivated by generalized versions of statistical mechanics with temperature fluctuations, provide excellent fits for the measured flux of all nuclei considered. Primary and secondary fluxes reveal a universal dependence on kinetic energy per nucleon for which the underlying energy distribution functions are solely distinguished by their effective degrees of freedom. All given spectra are characterized by a universal mean temperature parameter $\sim$ 200 MeV which agrees with the Hagedorn temperature. Our analysis suggests that QCD scattering processes together with nonequilibrium temperature fluctuations provide a plausible explanation for the observed universality in cosmic ray energy spectra. Our analysis suggests that QCD scattering processes together with nonequilibrium temperature fluctuations imprint universally onto the measured cosmic ray spectra, and produce a similar shape of energy spectra as high energy collider experiments on the Earth.

16 pages, 3 figures

References in corpus (4)

• From QCD-based hard-scattering to nonextensive statistical mechanical descriptions of transverse momentum spectra in high-energy $pp$ and $p\bar{p}$ collisions
• Defect turbulence and generalized statistical mechanics
• Statistics of 3-dimensional Lagrangian turbulence
• Superstatistics in high energy physics: Application to cosmic ray energy spectra and e+e- annihilation

Cited by in corpus (4)

• Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning
• Connecting complex networks to nonadditive entropies
• Spatio-temporal complexity of power-grid frequency fluctuations
• Fluctuations of water quality time series in rivers follow superstatistics