CART Method for Modeling the Output Power of Copper Bromide Laser

This paper examines the available experiment data for a copper bromide vapor laser (CuBr laser), emitting at two wavelengths - 510.6 and 578.2nm. Laser output power is estimated based on 10 independent input physical parameters. A classification and regression tree (CART) model is obtained which describes 97% of data. The resulting binary CART tree specifies which input parameters influence considerably each of the classification groups. This allows for a technical assessment that indicates which of these are the most significant for the manufacture and operation of the type of laser under consideration. The predicted values of the laser output power are also obtained depending on classification. This aids the design and development processes considerably.

• Iliycho P. Iliev
• Desislava S. Voynikova
• Snezhana G. Gocheva-Ilieva

• Classification and regression trees (CART)
• Copper Bromide laser (CuBr laser)
• laser generation
• nonparametric statistical model.

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