We present a neural network for the solution of the inverse swarm problem of deriving cross sections from swarm transport data. To account for the uncertainty inherent to this somewhat ill-posed inverse problem, we train the neural network using cross sections from the LXCat project, paired with associated transport coefficients found by the numerical solution of Boltzmann's equation. The use of experimentally measured and theoretically calculated cross sections for training encourages the network to avoid unphysical solutions, such as those containing spurious energy-dependent oscillations. We successfully apply this machine learning approach to simulated swarm data for electron transport in helium, separately determining its elastic momentum transfer and ionisation cross sections to within an accuracy of $4\%$ over the range of energies considered. Our attempt to extend our method to argon was less successful, although the reason for that observation is well-understood. Finally, we explore the feasibility of simultaneously determining cross sections of helium using this approach. We have some success here, determining elastic, total $n=2$ excitation and ionisation cross sections to $10\%$, $20\%$ and $25\%$ accuracy, respectively. We are unsuccessful in properly unfolding the separate $n=2$ singlet and triplet excitation cross sections of helium, but this is as expected given their similar threshold energies.

中文翻译：

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使用深度神经网络从传输系数确定横截面

我们提出了一个神经网络，用于解决从群传输数据导出横截面的逆群问题。为了解决这个有点不适定的逆问题固有的不确定性，我们使用来自 LXCat 项目的横截面训练神经网络，并结合通过 Boltzmann 方程的数值解找到的相关传输系数。使用实验测量和理论计算的横截面进行训练鼓励网络避免非物理解决方案，例如那些包含虚假能量依赖振荡的解决方案。我们成功地将这种机器学习方法应用于氦中电子传输的模拟群数据，在所考虑的能量范围内，分别确定其弹性动量传递和电离截面，精度为 $4\%$。我们将我们的方法扩展到氩气的尝试不太成功，尽管这种观察的原因是众所周知的。最后，我们探讨了使用这种方法同时确定氦横截面的可行性。我们在这方面取得了一些成功，确定弹性、总 $n=2$ 激发和电离截面分别达到 $10\%$、$20\%$ 和 $25\%$ 精度。我们未能正确展开氦的单独的 $n=2$ 单线态和三线态激发截面，但鉴于它们具有相似的阈值能量，这符合预期。最后，我们探讨了使用这种方法同时确定氦横截面的可行性。我们在这方面取得了一些成功，确定弹性、总 $n=2$ 激发和电离截面分别达到 $10\%$、$20\%$ 和 $25\%$ 精度。我们未能正确展开氦的单独的 $n=2$ 单线态和三线态激发截面，但鉴于它们具有相似的阈值能量，这符合预期。最后，我们探讨了使用这种方法同时确定氦横截面的可行性。我们在这方面取得了一些成功，确定弹性、总 $n=2$ 激发和电离截面分别达到 $10\%$、$20\%$ 和 $25\%$ 精度。我们未能正确展开氦的单独的 $n=2$ 单线态和三线态激发截面，但鉴于它们具有相似的阈值能量，这符合预期。