Application of the Classification and Regression Trees for Modeling the Laser Output Power of a Copper Bromide Vapor Laser

Abstract EN

This study examines the available experiment data for a copper bromide vapor laser (CuBr laser), emitting in the visible spectrum at 2 wavelengths—510.6 and 578.2 nm.

Laser output power is estimated based on 10 independent input parameters.

The CART method is used to build a binary regression tree of solutions with respect to output power.

In the case of a linear model, an approximation of 98% has been achieved and 99% for the model of interactions between predictors up to the the second order with an relative error under 5%.

The resulting CART tree takes into account which input quantities influence the formation of classification groups and in what manner.

This makes it possible to estimate which ones are significant from an engineering point of view for the development and operation of the considered type of lasers, thus assisting in the design and improvement of laser technology.