30 August 2026 to 4 September 2026
Asia/Tokyo timezone

A minimal two-population model of cosmic ray spectra at TeV-PeV energies

Not scheduled
20m
Oral Cosmic-rays

Speaker

Igor Vaiman (Gran Sasso Science Institute; INFN, Laboratori Nazionali del Gran Sasso)

Description

Latest measurements of spectra of different cosmic ray elements at TeV–PeV energies, as well as the all-particle spectrum and mean logarithmic mass, provide a wealth of new information for models of Galactic cosmic ray acceleration and propagation. At the same time, a consistent interpretation of all available datasets remains challenging, and different theoretical frameworks are often not readily distinguishable in the data. In light of this, we seek to build a minimal phenomenological model that self-consistently explains all observables in the chosen energy range as measured by the relevant experiments. The model is built on basic physical assumptions: power-law injection spectra, modified during propagation in a rigidity-dependent way. We perform a global fit to all available datasets, accounting for energy-scale uncertainties and consistency between elemental and all-particle measurements. We show conclusively that the data cannot be adequately described by a single-population model, in which all observed spectral features are attributed to propagation effects, even without including the latest LHAASO measurements of proton and helium spectra. A two-population model emerges as the next minimal alternative, featuring one population with lower maximum energy that provides the bulk of the CR energy density (SNR-like), and a second population of more energetic but rarer sources (e.g., microquasar-like). The hardening of elemental spectra around ~150 TV is produced by the interplay between the two populations. The softenings at ~13 TV and ~3 PV (the knee) are instead interpreted as genuine breaks in populations’ spectra, arising either from propagation effects or from maximum acceleration energies.

Primary authors

Carmelo Evoli (Gran Sasso Science Institute (GSSI, Italy)) Igor Vaiman (Gran Sasso Science Institute; INFN, Laboratori Nazionali del Gran Sasso)

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