Statistical behaviour of laser-induced plasma and its complementary characteristic signals

Abstract

In this work, we present a study aimed at the statistical distribution of characteristic signals of laser-induced plasmas. This work mainly focuses on observing statistical distribution for repetitive measurement of spectra, plasma plume imaging, and sound intensity. These were captured by using various laser irradiances, spanning between 1.72 and 6.25 GW cm⁻² for a 266 nm laser. Their distributions were fitted by Gaussian, generalized extreme value (GEV), and Burr distributions, as typical representation models used in LIBS. These were compared using the Kolmogorov–Smirnov (KS) test by its null hypothesis on whether these models are suitable or fail to describe the statistical distribution of the data. The behavior of the data distribution has shown a certain connection to the plasma plume temperature. This was observed for all the used ablation energies. Performances of the statistical models were further compared in the outlier filtering process, where the relative standard deviation of the filtered data was observed. The results presented in this work suggest that an appropriate selection of a statistical model for the data representation can lead to an improvement in the LIBS performance.