اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدInverting cosmic ray propagation by Convolutional Neural Networks
98
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
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.
قيم البحث
اقرأ أيضاً
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-ran
ge 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.
Feature representations, both hand-designed and learned ones, are often hard to analyze and interpret, even when they are extracted from visual data. We propose a new approach to study image representations by inverting them with an up-convolutional
neural network. We apply the method to shallow representations (HOG, SIFT, LBP), as well as to deep networks. For shallow representations our approach provides significantly better reconstructions than existing methods, revealing that there is surprisingly rich information contained in these features. Inverting a deep network trained on ImageNet provides several insights into the properties of the feature representation learned by the network. Most strikingly, the colors and the rough contours of an image can be reconstructed from activations in higher network layers and even from the predicted class probabilities.
We review numerical methods for simulations of cosmic ray (CR) propagation on galactic and larger scales. We present the development of algorithms designed for phenomenological and self-consistent models of CR propagation in kinetic description based
on numerical solutions of the Fokker-Planck equation. The phenomenological models assume a stationary structure of the galactic interstellar medium and incorporate diffusion of particles in physical and momentum space together with advection, spallation, production of secondaries and various radiation mechanisms. The self-consistent propagation models of CRs include the dynamical coupling of the CR population to the thermal plasma. The CR transport equation is discretized and solved numerically together with the set of magneto-hydrodynamic (MHD) equations in various approaches treating the CR population as a separate relativistic fluid within the two-fluid approach or as a spectrally resolved population of particles evolving in physical and momentum space. The relevant processes incorporated in self-consistent models include advection, diffusion and streaming well as adiabatic compression and several radiative loss mechanisms. We discuss applications of the numerical models for the interpretation of CR data collected by various instruments. We present example models of astrophysical processes influencing galactic evolution such as galactic winds, the amplification of large-scale magnetic fields and instabilities of the interstellar medium.
In this work, we considered 2 schemes (a high-rigidity break in primary source injections and a high-rigidity break in diffusion coefficient) to reproduce the newly released AMS-02 nuclei spectra (He, C, N, O, Li, Be, and B) when the rigidity larger
than 50 GV. The fitting results show that current data set favors a high-rigidity break at $sim 325 mathrm{GV}$ in diffusion coefficient rather than a break at $sim 365 mathrm{GV}$ in primary source injections. Meanwhile, the fitted values of the factors to rescale the cosmic-ray (CR) flux of secondary species/components after propagation show us that the secondary flux are underestimated in current propagation model. It implies that we might locate in a slow diffusion zone, in which the CRs propagate with a small value of diffusion coefficient compared with the averaged value in the galaxy. Another hint from the fitting results show that extra secondary CR nuclei injection may be needed in current data set. All these new hints should be paid more attention in future research.
The energy spectra of primary and secondary cosmic rays (CR) generally harden at several hundreds of GeV, which can be naturally interpreted by propagation effects. We adopt a spatially dependent CR propagation model to fit the spectral hardening, wh
ere a slow-diffusion disk (SDD) is assumed near the Galactic plane. We aim to constrain the propagation parameters with the Bayesian parameter estimation based on a Markov chain Monte Carlo sampling algorithm. The latest precise measurements of carbon spectrum and B/C ratio are adopted in the Bayesian analysis. The $rm{^{10}Be/^{9}Be}$ and Be/B ratios are also included to break parameter degeneracies. The fitting result shows that all the parameters are well constrained. Especially, the thickness of the SDD is limited to 0.4-0.5 kpc above and below the Galactic plane, which could be the best constraint for the slow-diffusion region among similar works. The $bar{p}/p$ ratio and amplitude of CR anisotropy predicted by the SDD model are consistent with the observations, while the predicted high-energy electron and positron fluxes are slightly and significantly lower than the observations, respectively, indicating the necessity of extra sources.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
