This study demonstrates the high capabilities of data mining and the random forest (RF) machine learning technique for processing experimental data in the field of laser equipment and technology and extracting significant information from these. The subject of study is the copper bromide vapor laser, used as a brightness amplifier and as an active medium in active optical systems actively developed in recent years. Published data from 465 experiments on this type of laser are statistically examined. RF regression models are built to predict the output power as a basic laser characteristic. The dependence of the output power on the input electric power, the pulse repetition frequency, the pressure of the additional gases in the discharge, and other operating and geometric parameters of the laser is determined. The models fit up to 98% of the experimentally measured laser output power data.

这项研究证明了数据挖掘和随机森林（RF）机器学习技术在处理激光设备和技术领域中的实验数据并从中提取重要信息的能力。研究的主题是溴化铜蒸气激光器，用作亮度放大器和近年来积极开发的有源光学系统中的有源介质。对来自465种此类激光器的实验的公开数据进行了统计检查。建立RF回归模型来预测输出功率，将其作为基本的激光特性。确定输出功率对输入电功率，脉冲重复频率，放电中其他气体的压力以及激光器的其他工作和几何参数的依赖性。