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| Paper: |
Automated Spectroscopic Analysis using Genetic Algorithms |
| Volume: |
535, Astronomical Data Analysis Software and Systems XXXI |
| Page: |
37 |
| Authors: |
Long, M.; Lau, M. L.; Zhang, S.; Ji L.; Xiao, J. |
| Abstract: |
We developed an automated spectroscopic analysis tool based on Genetic Algorithms (GA) to accelerate spectral fitting with reduced human intervention but improved efficiency and reproducibility. This automation is vital in the era when current and upcoming detectors acquire massive data at rates orders of magnitude greater than current collection rates. We use GA, a robust metaheuristic method to evaluate the optimization problem of fitting and obtaining global optima of parameters from various physical models. We also provide several types of selection operators to avoid the ill-conditioning the GA operators may encounter in specific applications. The method has been applied to X-ray spectral data of a starburst galaxy NGC 253 from the XMM-Newton reflection-grating spectrometer. We considered physical models including thermal emission from the diffuse hot plasma and the charge exchange emission due to its interaction with the cold gas. The results match well with manual fitting using Xspec. |
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