No Arabic abstract
The sources of ultrahigh-energy cosmic rays (UHECRs) have been difficult to catch. It was recently pointed out that while sources of UHECR protons exhibit anisotropy patterns that become denser and compressed with rising energy, nucleus-emitting-sources give rise to a cepa stratis (onion-like) structure with layers that become more distant from the source position with rising energy. The peculiar shape of the hot spots from nucleus-accelerators is steered by the competition between energy loss during propagation and deflection on the Galactic magnetic field (GMF). Here, we run a full-blown simulation study to accurately characterize the deflections of UHECR nuclei in the GMF. We show that while the cepa stratis structure provides a global description of anisotropy patterns produced by UHECR nuclei en route to Earth, the hot spots are elongated depending on their location in the sky due to the regular structure of the GMF. We demonstrate that with a high-statistics sample at the high-energy-end of the spectrum, like the one to be collected by NASAs POEMMA mission, the energy dependence of the hot-spot contours could become a useful observable to identify the nuclear composition of UHECRs. This new method to determine the nature of the particle species is complementary to those using observables of extensive air showers, and therefore is unaffected by the large systematic uncertainties of hadronic interaction models.
We present a novel method to search for structures of coherently aligned patterns in ultra-high energy cosmic-ray arrival directions simultaneously across the entire sky. This method can be used to obtain information on the Galactic magnetic field, in particular the integrated component perpendicular to the line of sight, from cosmic-ray data only. Using a likelihood-ratio approach, neighboring cosmic rays are related by rotatable, elliptically shaped density distributions and the significance of their alignment with respect to circular distributions is evaluated. In this way, a vector field tangential to the celestial sphere is fitted which approximates the local deflections in cosmic magnetic fields if significant deflection structures are detected. The sensitivity of the method is evaluated on the basis of astrophysical simulations of the ultra-high energy cosmic-ray sky, where a discriminative power between isotropic and signal-induced scenarios is found.
The Pierre Auger Collaboration has reported evidence for anisotropy in the distribution of arrival directions of the cosmic rays with energies $E>E_{th}=5.5times 10^{19}$ eV. These show a correlation with the distribution of nearby extragalactic objects, including an apparent excess around the direction of Centaurus A. If the particles responsible for these excesses at $E>E_{th}$ are heavy nuclei with charge $Z$, the proton component of the sources should lead to excesses in the same regions at energies $E/Z$. We here report the lack of anisotropies in these directions at energies above $E_{th}/Z$ (for illustrative values of $Z=6, 13, 26$). If the anisotropies above $E_{th}$ are due to nuclei with charge $Z$, and under reasonable assumptions about the acceleration process, these observations imply stringent constraints on the allowed proton fraction at the lower energies.
We propose a new method for the estimation of ultra-high energy cosmic ray (UHECR) mass composition from a distribution of their arrival directions. The method employs a test statistic (TS) based on a characteristic deflection of UHECR events with respect to the distribution of luminous matter in the local Universe. Making realistic simulations of the mock UHECR sets, we show that this TS is robust to the presence of galactic and non-extreme extra-galactic magnetic fields and sensitive to the mass composition of events in a set. This allows one to constrain the UHECR mass composition by comparing the TS distribution of a composition model in question with the data TS, and to discriminate between different composition models. While the statistical power of the method depends somewhat on the MF parameters, this dependence decreases with the growth of statistics. The method shows good performance even at GZK energies where the estimation of UHCER mass composition with traditional methods is complicated by a low statistics.
Energy-dependent patterns in the arrival directions of cosmic rays are searched for using data of the Pierre Auger Observatory. We investigate local regions around the highest-energy cosmic rays with $E geq 6 cdot 10^{19}$ eV by analyzing cosmic rays with energies above $E = 5 cdot 10^{18}$ eV arriving within an angular separation of approximately $15{deg}$. We characterize the energy distributions inside these regions by two independent methods, one searching for angular dependence of energy-energy correlations and one searching for collimation of energy along the local system of principal axes of the energy distribution. No significant patterns are found with this analysis. The comparison of these measurements with astrophysical scenarios can therefore be used to obtain constraints on related model parameters such as strength of cosmic-ray deflection and density of point sources.
The Pierre Auger Collaboration (Auger) recently reported a correlation between the arrival directions of cosmic rays with energies above 39 EeV and the flux pattern of 23 nearby starburst galaxies (SBGs). In this Letter, we tested the same hypothesis using cosmic rays detected by the Telescope Array experiment (TA) in the 9-year period from May 2008 to May 2017. Unlike the Auger analysis, we did not optimize the parameter values but kept them fixed to the best-fit values found by Auger, namely 9.7% for the anisotropic fraction of cosmic rays assumed to originate from the SBGs in the list and 12.9{deg} for the angular scale of the correlations. The energy threshold we adopted is 43 EeV, corresponding to 39 EeV in Auger when taking into account the energy-scale difference between two experiments. We find that the TA data is compatible with isotropy to within 1.1{sigma} and with the Auger result to within 1.4{sigma}, meaning that it is not capable to discriminate between these two hypotheses.