Towards Optimal Classifier of Spectroscopy Data

Laser spectroscopy can produce vast amounts of data, anticipating needs for automatization of tasks such as classification and discrimination of spectra. Using the apparatus of statistical theory of detection, we develop the optimal classifier for spectroscopy data for a linear model of an echelle spectrograph system. We validate model assumptions through statistical analysis of “dark signal” and laser-breakdown induced spectra of standardized NIST glass. The experimental results suggest that the quadratic classifier may provide optimal performance if the spectroscopy signal and noise can be considered Gaussian.