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| Paper: |
Astrophysical Atomic Data in the UV Using Computational Intelligence Algorithms |
| Monograph: |
11, HWO25 Proceedings Part II: Mission Framework, Technology, and Broader Contributions |
| Page: |
315 |
| Authors: |
Joshua Emanuel Lara Sabala, Alicia Morales Reyes, Emanuele Bertone, and Miguel Chávez Dagostino |
| DOI: |
10.26624/TLNW9919 |
| Abstract: |
Accurate atomic data are critical for reliable modeling of spectral lines. However, many atomic parameters, such as central wavelength, oscillator strengths and damping constants, remain poorly constrained, limiting the precision of synthetic spectra, and consequently our ability to interpret observations. In this study, we demonstrate the feasibility of using evolutionary optimization techniques to calibrate the atomic data for transitions in the ultraviolet (UV) range, where line blending and parameter degeneracy severely complicate the analysis. We compared several methods and found that Genetic Algorithms, particularly with a cooperative coevolution (CC) strategy, yield the most accurate results, with a final discrepancy of 1.47% between the reference and optimized spectral flux. Furthermore, the convergence of the atomic data towards the correct reference values was satisfactory. This approach offers a promising path to improve atomic databases for UV spectral synthesis. |
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