Application of genetic algorithms to multi-objective data analysis in plasma x-ray spectroscopy

X-ray spectroscopy is a powerful diagnostic of plasma conditions. The associated data analysis requires the solution of inversion problems whose complexity and difficulty depends on the type and number of pieces of data that have to be taken into account. Recent advances in x-ray instrumentation have enabled the observation of arrays of spectrally, spatially, and time resolved data that have been key to unfold the spatial structure of inertial confinement fusion (ICF) implosion cores, i.e. a tomographic reconstruction of temperature and density spatial profiles. The information is encoded in the atomic and radiation transport physics that determines the observed radiation from the plasma. The data analysis requires the simultaneous and self-consistent consideration of a large number of photon-energy resolved x-ray intensity distributions, and search and optimization in a multi-dimensional parameter space. We discuss the application of the Pareto genetic algorithm to the solution of this data analysis problem, and illustrate the results with data from ICF implosion experiments performed at the OMEGA laser facility. The case of illustration is specific but the ideas and methodology developed are of general application.