Dynamic Effects in Electron Momentum Spectroscopy of Sulfur Hexafluoride

Electron momentum spectroscopy (EMS) results are presented for the sulfur hexafluoride (SF6) molecule using a high-resolution binary (e, 2e) spectrometer at incident energies (Ei) of 600, 1200, and 2400 eV plus the binding energy. The valence orbital momentum profiles were measured with a binding energy resolution of 0.68 eV and angular resolutions of Δθ = ±0.6⁰, ΔΦ = ±0.85⁰. Whereas the two higher incident energies are in the range where normally EMS measurements do not exhibit an impact-energy dependence, the current experimental data display a dynamic dependence on the impact energies. The measured momentum profiles are compared with predictions from a plane-wave impulse approximation (PWIA) calculation using molecular orbitals obtained from a density-functional-theory quantum-chemistry calculation. The PWIA calculations are in fairly good agreement with experiment only for 2400 eV impact energy, particularly for the summed 1t2u and 5t1u orbitals. We have also compared the experimental results for the 5a1g state with the molecular three-body distorted-wave (M3DW) approach using the orientation-averaged molecular orbital approximation. Unlike the PWIA, the M3DW results are in very good agreement with the experimental data at all three measured incident energies for small momenta, which indicates that dynamical distortion effects are important for this molecule

Low Energy (e, 2e) Study from the 1t₂ Orbital of Ch₄

Single ionization of the methane (CH4) 1t2 orbital by 54 eV electron impact has been studied experimentally and theoretically. The measured triple differential cross sections cover nearly a 4π solid angle for the emission of low energy electrons and a range of projectile scattering angles. Experimental data are compared with theoretical calculations from the distorted wave Born approximation and the molecular three-body distorted wave models. It is found that theory can give a proper description of the main features of experimental cross section only at smaller scattering angles. For larger scattering angles, significant discrepancies between experiment and theory are observed. The importance of the strength of nuclear scattering from the H-nuclei was theoretically tested by reducing the distance between the carbon nuclei and the hydrogen nuclei and improved agreement with experiment was found for both the scattering plane and the perpendicular plane

Picturing Electron Capture to the Continuum in the Transfer Ionization of Intermediate-Energy He²⁺ Collisions with Argon

Electron emission occurring in transfer ionization for He2+ collisions with argon has been investigated using cold target recoil ion momentum spectroscopy. The double differential cross sections for electron capture to the continuum of the projectile (cusp-shaped electrons) are presented for collision energies from 17.5 to 75 keV/u. For an energy of 30 keV/u, we find a maximum in the experimental ratio of the cusp-shaped electron yield to the total electron yield. This result is explained in terms of the velocity matching between the projectile ion and the electron initially bound to the target. One of the important issues for double electron transitions is the role of electron-electron correlation. If this correlation is weak, then the transfer-ionization process can be viewed as two separate sequential processes. If this correlation is strong, then the transfer-ionization process would happen simultaneously and not sequentially. Our experimental and theoretical results indicate that correlation is weak and that the first step is target ionization followed by charge capture

Image classification model based on spark and CNN

Convolution neural network is a commonly used image classification model, but when the network nodes of the training process are too many, it will have a great influence on the training complexity. At the same time, when the size of the image data is large, many problems will appear on the single node, such as convergence slowly, frequently disk reading and writing. In order to overcome the above problems, this paper proposes a distributed convolution neural network based on Spark (Distribution Convolution neural network, Dis-CNN) model. The model first improves the initialization mode of convolution kernel parameters, then eliminates the redundancy of feature maps, and finally optimizes the distributed gradient descent by reducing the synchronous traffic between master and slave, so as to improve the convergence speed and performance. The experimental results show that the model not only improves the accuracy and recall of image classification, but also performs excellent in parallelism

Image classification model based on spark and CNN

Convolution neural network is a commonly used image classification model, but when the network nodes of the training process are too many, it will have a great influence on the training complexity. At the same time, when the size of the image data is large, many problems will appear on the single node, such as convergence slowly, frequently disk reading and writing. In order to overcome the above problems, this paper proposes a distributed convolution neural network based on Spark (Distribution Convolution neural network, Dis-CNN) model. The model first improves the initialization mode of convolution kernel parameters, then eliminates the redundancy of feature maps, and finally optimizes the distributed gradient descent by reducing the synchronous traffic between master and slave, so as to improve the convergence speed and performance. The experimental results show that the model not only improves the accuracy and recall of image classification, but also performs excellent in parallelism