Spectrum and anisotropies of Galactic cosmic rays: a laboratory for magnetic fields

TL;DR

The paper addresses how Galactic cosmic rays encode information about Galactic magnetic fields through their spectra and anisotropies across wide energy ranges. It interprets data within a diffusive transport framework, where propagated spectra satisfy $oldsymbol{ackslash psi}_j(oldsymbol{ackslash R}) \\propto q_j(oldsymbol{ackslash R}) / oldsymbol{ackslash kappa}(oldsymbol{ackslash R})$, and uses secondary species as a discriminant to distinguish source versus transport origins for spectral breaks; it also analyzes large-scale and small-scale anisotropies, including Compton-Getting and streaming effects, to probe local magnetic structure and turbulence. A key finding is that the prominent hardening near a few hundred GV is favored to arise from transport breaks in $oldsymbol{ackslash kappa}(oldsymbol{ackslash R})$ rather than intrinsic source breaks, with secondaries often hardening by about twice the primaries in transport scenarios; leptons reveal the impact of energy losses and nearby sources, while small-scale anisotropies reflect local turbulence and last-scattering conditions. Together, spectra and anisotropies enable Galactic cosmic rays to function as a laboratory for mapping ordered and turbulent magnetic fields, with future high-precision data and refined transport and source models driving tighter constraints on the turbulence spectrum and the local magnetic geometry.

Abstract

Much has been learned about Galactic cosmic rays in the past decade: On the observational side, the spectra of cosmic ray nuclei have been directly measured with high precision, resolving chemical composition up to TV rigidities. At even higher rigidities, direct detection is making contact with indirect observations from air shower arrays. A number of breaks have been found in the nuclear spectrum, which was previously thought to be a pure power law up to the knee. Data from air shower arrays also show interesting features in the arrival directions of cosmic-ray nuclei. On the theoretical side, more sophisticated models are able to explain the various spectral breaks either with transitions between different classes of sources or with changes in the transport regime. Yet, it has become clear that our ignorance of the structure of the Galactic magnetic fields, both on large and small scales, is limiting precision predictions. Turning this problem into an opportunity though, we can use Galactic cosmic rays as a laboratory for the study of Galactic magnetic fields. In this review talk, delivered at the 39th International Cosmic Ray Conference (ICRC2025), I have summarised what is known about the spectrum and anisotropies of Galactic cosmic rays, what is not known yet and what can be learnt in the future.

Figures (10)

• Figure 1: The differential intensity $\mathrm{d} J / \mathrm{d} E$ of cosmic rays as a function of kinetic energy $E$. The data on charged cosmic rays have been retrieved from the Cosmic-Ray Database (CRDB) Maurin:2023alp and from Refs. LHAASO:2024knt. Data on gamma-rays and neutrinos are from Refs. Fermi-LAT:2012edvFermi-LAT:2014ryhLHAASO:2023gneIceCube:2023qpn.
• Figure 2: The proton and Helium rigidity spectra between a GV and $\sim 10 \, \text{PV}$. The intensity $\mathrm{d} J / \mathrm{d} \mathcal{R}$ has been scaled with $\mathcal{R}^{2.7}$ to bring out the various spectral features. The data have been retrieved from the Cosmic-Ray Database (CRDB) Maurin:2023alp and from Refs. GRAPES-3:2024mhyLHAASO:2025byy.
• Figure 3: The spectra of primary (left panel) and secondary GCRs (right panel). The intensity $\mathrm{d} J / \mathrm{d} \mathcal{R}$ has been scaled with $\mathcal{R}^{2.7}$ to bring out the various spectral features. The data have been retrieved from the Cosmic-Ray Database (CRDB) Maurin:2023alp and from Refs. GRAPES-3:2024mhyLHAASO:2025byy.
• Figure 4: Left: Illustration of the superposition of the contributions from different source classes, generically labelled 'A', 'B' and 'C'. Right: Superposition of the spectrum from a background population and one individual source, in this example a pulsar wind nebulae.
• Figure 5: Formation of breaks in primary and secondary spectra, either due to a break in the source spectrum (top) or a break in the diffusion coefficient (bottom)