Constraining the position of the knee in the galactic cosmic ray spectrum with ultra-high-energy diffuse γ\gamma-rays

The diffuse $\gamma$-ray emission was measured up to $957$ TeV by the Tibet-AS$\gamma$ experiment recently. Assuming that it is produced by the hadronic interaction between cosmic ray nuclei and the interstellar medium, it requires that the cosmic ray nuclei should be accelerated well beyond PeV energies. Measurements of the cosmic ray spectra for different species show diverse results at present. The Tibet experiments showed that the spectrum of proton plus helium has an early knee below PeV. If this is correct, the diffuse $\gamma$-ray emission would suggest an additional component of Galactic cosmic rays above PeV energies. This second component may originate from a source population of so-called PeVatrons revealed by recent ultra-high energy $\gamma$-ray observations, and could contribute to the cosmic ray fluxes up to the energy of the second knee. On the other hand, the KASCADE measurement showed that the knee of protons is higher than PeV. In this case, the diffuse $\gamma$-rays observed by Tibet-AS$\gamma$ can be well accounted for by only one cosmic ray component. These two scenarious (ie. the Tibet and KASCADE knees) could be distinguished by the spectral structures of diffuse $\gamma$-rays and cosmic ray nuclei. Future measurements of spectra of individual nuclei by HERD and LHAASO experiments and diffuse $\gamma$-rays by LHAASO can jointly constrain these two scenarios.Comment: 9 pages,4 figures. accepted by Ap

Deep functional factor models: forecasting high-dimensional functional time series via Bayesian nonparametric factorization

This paper introduces the Deep Functional Factor Model (DF2M), a Bayesian nonparametric model designed for analysis of high-dimensional functional time series. DF2M is built upon the Indian Buffet Process and the multi-task Gaussian Process, incorporating a deep kernel function that captures non-Markovian and nonlinear temporal dynamics. Unlike many black-box deep learning models, DF2M offers an explainable approach to utilizing neural networks by constructing a factor model and integrating deep neural networks within the kernel function. Additionally, we develop a computationally efficient variational inference algorithm to infer DF2M. Empirical results from four real-world datasets demonstrate that DF2M provides better explainability and superior predictive accuracy compared to conventional deep learning models for high-dimensional functional time series

Bethe states of the trigonometric SU(3) spin chain with generic open boundaries

By combining the algebraic Bethe ansatz and the off-diagonal Bethe ansatz, we investigate the trigonometric SU(3) model with generic open boundaries. The eigenvalues of the transfer matrix are given in terms of an inhomogeneous T-Q relation, and the corresponding eigenstates are expressed in terms of nested Bethe-type eigenstates which have well-defined homogeneous limit. This exact solution provides a basis for further analyzing the thermodynamic properties and correlation functions of the anisotropic models associated with higher rank algebras.Comment: 17 pages, 3 tables. arXiv admin note: text overlap with arXiv:1705.0947

Constitutive model of 7075 aluminum at high temperature

In order to obtain the accurate mechanical properties of 7075 aluminum alloy, the Gleeble-1500D thermal simulation test machine was used to perform compression test on 7075 aluminum alloy. The deformation temperature range is 490 °C~560 °C, and the strain rate is 0,001 s-1~1 s-1. At present, for the high temperature thermal compression process, the Arrhenius constitutive model with strain compensation is usually used. The results show that the correlation coefficient of the Arrhenius constitutive model of 7075 aluminum alloy with strain compensation is 0,9894, and the average relative error is 5,6 %, realizing the fitting of flow stress and prediction, verified the feasibility of the model

Constitutive model of 7075 aluminum at high temperature

In order to obtain the accurate mechanical properties of 7075 aluminum alloy, the Gleeble-1500D thermal simulation test machine was used to perform compression test on 7075 aluminum alloy. The deformation temperature range is 490 °C~560 °C, and the strain rate is 0,001 s-1~1 s-1. At present, for the high temperature thermal compression process, the Arrhenius constitutive model with strain compensation is usually used. The results show that the correlation coefficient of the Arrhenius constitutive model of 7075 aluminum alloy with strain compensation is 0,9894, and the average relative error is 5,6 %, realizing the fitting of flow stress and prediction, verified the feasibility of the model