Sparling Chris, Onvlee Jolijn
Institute for Molecules and Materials, Radboud Universiteit, Heyendaalseweg 135, 6525 AJ, Nijmegen, The Netherlands.
Phys Chem Chem Phys. 2025 Aug 20. doi: 10.1039/d5cp00857c.
The velocity-map imaging (VMI) technique is used near ubiquitously throughout the study of gas-phase photophysics and chemical dynamics. Many VMI experiments rely on numerical reconstruction techniques to recover the full three-dimensional (3D) velocity distribution of photoproducts from the two-dimensional (2D) geometric projection - the Abel transform of the distribution - that is recorded in a typical experiment. The simplest mathematical approach for this reconstruction procedure is through use of the inverse Abel integral transform. Historically, though, this approach has performed poorly on real experimental data, and so the VMI community has devoted much effort into the development of alternative inversion strategies that avoid direct use of the integral. In this article, we challenge this firmly held belief, and show instead what advantages can be realised through this approach. Unlike many other competing approaches, the reconstruction technique presented here, which we refer to as the modified Abel integral transform (MAIT), does not require the lengthy pre-computation time for a large basis set or any manually adjustable regularisation parameters. Examples involving simulated and real experimental data are used to demonstrate the efficacy of our new approach. This method is shown to perform similarly to the most popular alternative strategies for extracting photoproduct angular distributions, and have a significant advantage over them when handling data with high levels of background noise, in particular.
速度映射成像（VMI）技术几乎在气相光物理和化学动力学研究的各个方面都有应用。许多VMI实验依靠数值重建技术，从二维（2D）几何投影——典型实验中记录的分布的阿贝尔变换——来恢复光产物的完整三维（3D）速度分布。这种重建过程最简单的数学方法是使用阿贝尔积分逆变换。然而，从历史上看，这种方法在实际实验数据上的表现很差，因此VMI领域投入了大量精力来开发避免直接使用积分的替代反演策略。在本文中，我们对这种根深蒂固的观念提出挑战，转而展示通过这种方法可以实现哪些优势。与许多其他竞争方法不同，这里提出的重建技术，我们称之为修正阿贝尔积分变换（MAIT），不需要为大型基组进行长时间的预计算，也不需要任何手动调整的正则化参数。涉及模拟和实际实验数据的例子用于证明我们新方法的有效性。结果表明，该方法在提取光产物角分布方面的表现与最流行的替代策略类似，尤其在处理具有高背景噪声水平的数据时，比它们具有显著优势。
