通过基于群数据的神经网络分析改进的电子-THFA截面集
An improved set of electron-THFA cross sections refined through a neural network-based analysis of swarm data
摘要：
我们回顾实验和理论截面电子输运在$α$ -四氢糠醇（THFA），并在这样做，提出了一个合理的完整的设置。为了评估我们提出的设置的准确性和自洽性，我们使用的脉冲汤森技术测量漂移速度，纵向扩散系数和有效的汤森第一电离系数的电子群在混合物中的THFA在氩气中，在一个范围内的密度减少电场从1 Td到450 Td。这些测量值，然后比较来自我们提出的一套使用玻尔兹曼方程的多项解决方案的模拟值。我们观察到的模拟和实验之间的差异，我们试图通过采用一个神经网络模型，训练来解决逆群展开的截面支撑我们的实验群测量问题来解决。从我们的神经网络为基础的分析结果是一个完善的电子-THFA截面，我们确认是更高的一致性与我们的群体测量比我们最初提出的。我们还使用我们的数据库来计算纯THFA中的电子输运系数，跨越从0.001 Td到10，000 Td的减小的电场的范围。
英文摘要：
We review experimental and theoretical cross sections for electron transport in $α$-tetrahydrofurfuryl alcohol (THFA) and, in doing so, propose a plausible complete set. To assess the accuracy and self-consistency of our proposed set, we use the pulsed-Townsend technique to measure drift velocities, longitudinal diffusion coefficients and effective Townsend first ionisation coefficients for electron swarms in admixtures of THFA in argon, across a range of density-reduced electric fields from 1 Td to 450 Td. These measurements are then compared to simulated values derived from our proposed set using a multi-term solution of Boltzmann's equation. We observe discrepancies between the simulation and experiment, which we attempt to address by employing a neural network model that is trained to solve the inverse swarm problem of unfolding the cross sections underpinning our experimental swarm measurements. What results from our neural network-based analysis is a refined set of electron-THFA cross sections, which we confirm is of higher consistency with our swarm measurements than that we initially proposed. We also use our data base to calculate electron transport coefficients in pure THFA, across a range of reduced electric fields from 0.001 Td to 10,000 Td.
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