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Register a new userIdentification of Patterns in Cosmic-Ray Arrival Directions using Dynamic Graph Convolutional Neural Networks
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We present a new approach for the identification of ultra-high energy cosmic rays from sources using dynamic graph convolutional neural networks. These networks are designed to handle sparsely arranged objects and to exploit their short- and long-range correlations. Our method searches for patterns in the arrival directions of cosmic rays, which are expected to result from coherent deflections in cosmic magnetic fields. The network discriminates astrophysical scenarios with source signatures from those with only isotropically distributed cosmic rays and allows for the identification of cosmic rays that belong to a deflection pattern. We use simulated astrophysical scenarios where the source density is the only free parameter to show how density limits can be derived. We apply this method to a public data set from the AGASA Observatory.
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We propose a machine learning method to investigate the propagation of cosmic rays, based on the precisely measured spectra of primary and secondary nuclei Li, Be, B, C, and O by AMS-02, ACE, and Voyager-1. We train two Convolutional Neural Network machines: one learns how to infer the propagation and source parameters from the energy spectra of cosmic rays, and the other one is similar to the former but with flexibility of learning from the data with added artificial fluctuations. Together with the mock data generated by GALPROP, we find that both machines can properly invert the propagation process and infer the propagation and source parameters reasonably well. This approach can be much more efficient than the traditional Markov Chain Monte Carlo fitting method in deriving the propagation parameters if the users would like to update the confidence intervals with new experimental data. The trained models are also publicly available.
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 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.
Modern detectors of cosmic gamma-rays are a special type of imaging telescopes (air Cherenkov telescopes) supplied with cameras with a relatively large number of photomultiplier-based pixels. For example, the camera of the TAIGA-IACT telescope has 560 pixels of hexagonal structure. Images in such cameras can be analysed by deep learning techniques to extract numerous physical and geometrical parameters and/or for incoming particle identification. The most powerful deep learning technique for image analysis, the so-called convolutional neural network (CNN), was implemented in this study. Two open source libraries for machine learning, PyTorch and TensorFlow, were tested as possible software platforms for particle identification in imaging air Cherenkov telescopes. Monte Carlo simulation was performed to analyse images of gamma-rays and background particles (protons) as well as estimate identification accuracy. Further steps of implementation and improvement of this technique are discussed.
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.
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