Heim Pascal, Rumetshofer Michael, Ranftl Sascha, Thaler Bernhard, Ernst Wolfgang E, Koch Markus, von der Linden Wolfgang
Institute of Experimental Physics, Graz University of Technology, 8010 Graz, Austria.
Institute of Theoretical and Computational Physics, Graz University of Technology, 8010 Graz, Austria.
Entropy (Basel). 2019 Jan 19;21(1):93. doi: 10.3390/e21010093.
This paper employs Bayesian probability theory for analyzing data generated in femtosecond pump-probe photoelectron-photoion coincidence (PEPICO) experiments. These experiments allow investigating ultrafast dynamical processes in photoexcited molecules. Bayesian probability theory is consistently applied to data analysis problems occurring in these types of experiments such as background subtraction and false coincidences. We previously demonstrated that the Bayesian formalism has many advantages, amongst which are compensation of false coincidences, no overestimation of pump-only contributions, significantly increased signal-to-noise ratio, and applicability to any experimental situation and noise statistics. Most importantly, by accounting for false coincidences, our approach allows running experiments at higher ionization rates, resulting in an appreciable reduction of data acquisition times. In addition to our previous paper, we include fluctuating laser intensities, of which the straightforward implementation highlights yet another advantage of the Bayesian formalism. Our method is thoroughly scrutinized by challenging mock data, where we find a minor impact of laser fluctuations on false coincidences, yet a noteworthy influence on background subtraction. We apply our algorithm to data obtained in experiments and discuss the impact of laser fluctuations on the data analysis.
本文采用贝叶斯概率理论对飞秒泵浦-探测光电子-光离子符合（PEPICO）实验中产生的数据进行分析。这些实验有助于研究光激发分子中的超快动力学过程。贝叶斯概率理论被持续应用于这类实验中出现的数据分析问题，如背景扣除和错误符合。我们之前证明了贝叶斯形式体系有许多优点，其中包括对错误符合的补偿、不会高估仅泵浦的贡献、显著提高信噪比以及适用于任何实验情况和噪声统计。最重要的是，通过考虑错误符合，我们的方法允许在更高的电离率下进行实验，从而显著减少数据采集时间。除了我们之前的论文，我们还纳入了波动的激光强度，其直接的实现方式突出了贝叶斯形式体系的另一个优点。我们的方法通过挑战性模拟数据进行了全面检验，我们发现激光波动对错误符合的影响较小，但对背景扣除有显著影响。我们将我们的算法应用于实验获得的数据，并讨论激光波动对数据分析的影响。
