Determination of the distribution of strong coupling constant with machine learning

In this work, we use the artificial neural network (ANN) method to study and predict the distribution of strong coupling constants by fitting the existing data. Our approach takes advantage of the ability of ANN to learn complex nonlinear relations and excellent generalization, and allows for a systematic treatment of the uncertainties associated with the data. To ensure the reliability of our results, we apply cross-validation methods during the training process. Finally, we obtained the predicted values of the strong coupling constants at different energy scales, and compared and verified them with the existing experimental data. Our approach represents a promising way to improve the determination of the strong coupling constant at low energies, and could have important implications for future experimental and theoretical studies in quantum chromodynamics.Comment: 7 pages, 4 figure

Analysis of the interaction between ϕ\phi meson and nucleus

In this work, we systematically study the $\phi$ meson and nucleus interaction by analyzing and fitting the cross sections of $\gamma N$$\rightarrow \phi$$N$ ($N$ represent the nucleus) reactions near the threshold. With the help of vector meson dominant model, the distribution of $\phi$-$N$ scattering length as a function of energy is presented, and the results show that there is a slight increase in scattering length with increasing energy. Based on this, the average scattering length of $\phi$-proton is obtained as $0.10\pm0.01$ fm by combining experimental data and theoretical models. Moreover, the average scattering length of $\phi$-deuteron interaction is derived to be $0.014\pm0.002$ fm for the first time. Further, the effect of the momentum transfer $|t_{min}|$ on the $\phi$-$N$ scattering length at the threshold is discussed. The obtained results not only provide important theoretical information for a more comprehensive and accurate study of the $\phi$-$N$ scattering length, but also provide a basis for future experimental measurements of $\phi$ meson production.Comment: 9 pages, 12 figures. To be published in Chinese Physics

Mass radius and mechanical properties of the proton via strange ϕ\phi meson photoproduction

In this work, the cross sections of $\phi$ photoproduction are systematically investigated with the two gluon exchange model and the pomeron model. The results obtained show that the theoretical values of the two models agree well with the experimental data. Since the mass radius and mechanical properties of the proton are encoded in the scalar gravitational form factor of the momentum energy tensor, based on the differential cross sections of $\phi$ photoproduction at near-threshold predicted by the two gluon exchange model, the average mass radius of the proton is derived as $\sqrt{\left\langle r^{2}\right\rangle_\mathrm{m} }=0.78 \pm 0.06$ fm. As a comparison, we directly extract the proton mass radius from the experimental data of $\phi$ photoproduction to obtain an average value of $0.80\pm0.05$ fm, which is very close to the result given by the two gluon exchange model. Taking a similar approach, we extracted the average value of $m_{D}$ to obtain the distribution of the pressure and shear force contributed within the proton, and compared the results with other groups. The results of this paper may provide necessary theoretical information for the subsequent in-depth study of the internal structure and properties of proton.Comment: 11 pages, 13 figure

Boosting graph search with attention network for solving the general orienteering problem

Recently, several studies explore to use neural networks(NNs) to solve different routing problems, which is an auspicious direction. These studies usually design an encoder–decoder based framework that uses encoder embeddings of nodes and the problem-specific context to iteratively generate node sequence(path), and further optimize the produced result on top, such as a beam search. However, these models are limited to accepting only the coordinates of nodes as input, disregarding the self-referential nature of the studied routing problems, and failing to account for the low reliability of node selection in the initial stages, thereby posing challenges for real-world applications.In this paper, we take the orienteering problem as an example to tackle these limitations in the previous studies. We propose a novel combination of a variant beam search algorithm and a learned heuristic for solving the general orienteering problem. We acquire the heuristic with an attention network that takes the distances among nodes as input, and learn it via a reinforcement learning framework. The empirical studies show that our method can surpass a wide range of baselines and achieve results iteratively generate the optimal or highly specialized approach