A Semi-Autonomous Method to Detect Cosmic Rays in Raman Hyperspectral Data Sets

Abstract

Cosmic rays can degrade Raman hyperspectral images by introducing high-intensity noise to spectra, obfuscating the results of downstream analyses. We describe a novel method to detect cosmic rays in deep ultraviolet Raman hyperspectral data sets adapted from existing cosmic ray removal methods applied to astronomical images. This method identifies cosmic rays as outliers in the distribution of intensity values in each wavelength channel. In some cases, this algorithm fails to identify cosmic rays in data sets with high inter-spectral variance, uncorrected baseline drift, or few spectra. However, this method effectively identifies cosmic rays in spatially uncorrelated hyperspectral data sets more effectively than other cosmic ray rejection methods and can potentially be employed in commercial and robotic Raman systems to identify cosmic rays semi-autonomously.