Feasibility of Using Neural Networks and Other Optimization Algorithms to ObtainCross Sections from Electron Swarm Data

摘要

Three kinds of numerical optimization algorithms have been investigated for usein estimating the electron momentum transfer and excitation cross sections for atoms and molecules based on measured electron transport, or swarm data. The methods investigated are the downhill or creeping simplex; simulated annealing; and neural networks. These methods have been used to obtain the cross section for momentum transfer for a model system from E/N (Electric field, E, divided by the total gas density, N) dependent drift velocities and characteristic energies. In addition the creeping simplex has been used to obtain momentum transfer cross sections for the He and Ar and the momentum transfer cross section and a vibrational excitation cross section for methane from measured drift velocity and characteristic energy data. A neural network has been used to obtain an estimate of the momentum transfer cross section of xenon in the vicinity of the Ramsauer minimum from swarm data. These results serve as examples of what may be possible using these and, perhaps other optimization algorithms. Keywords: Draft velocity, Cross section, Numerical optimization, Simplex method. (jhd)