Ultraviolet Atomic Data Assessment from Astrophysical Observations. I. A Comparative Study of Evolutionary Algorithms

Abstract Accurate atomic parameters are essential for reliable spectral synthesis, particularly in the ultraviolet range, where line blending and parameter degeneracy pose significant challenges. In this feasibility test, we apply different optimization techniques to calibrate the atomic data of central wavelength, oscillator strength and damping parameters by minimizing the difference between the synthetic and reference spectra. We compare the performance of Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Cross-Entropy Algorithm (CE) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Our results show that GA and CMA-ES consistently outperform the other algorithms. We assessed the accuracy of the optimized parameters using its standard deviation from the algorithm executions. Using this criterion, a significant fraction (63 % of the central wavelengths and 48 % of oscillator strengths) for the strongest lines were recovered accurately. Within this subset, our approach yielded in some cases uncertainties comparable to or smaller than those reported by the NIST Atomic Spectra Database and the Vienna Atomic Line Database.In contrast, Van der Waals and Stark broadening parameters were severely affected by parameter degeneracy. These findings establish GA and CMA-ES as a reliable optimization method for this high-dimensional problem.