Presentation #102.34 in the session ISM/Galaxies.
We developed an automated spectral 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 start with a population of temporary solutions consisting of model parameters called chromosomes. We then select part of the optimal solutions by evaluating their fitness value and random solutions for mixing, i.e., crossover for the next generation of solutions. Next, we apply mutation operators to modify existing solutions by disturbing them by random chance. We also provide several types of selection operators to avoid ill-condition the GA operators may encounter. Our GA method has been applied to X-ray data of starburst galaxies like NGC 253 from XMM-Newton reflection grating spectrometer, and compared with manual fitting using Xspec. The GA fitting considers a set of potential physical models including thermal emission from the diffuse hot plasma as well as the charge exchange (CX) emission due to its interaction with the cold gas, besides the normal components of the bright point sources and the foreground absorption. It can automatically search for (1) a set of combined optimal line profiles (e.g., Gaussian) and (2) a set of combined APEC and ACX models and ultimately provide optimal fitting parameters. Our results show that both manual and GA fittings lead to good results but the GA method demonstrates a distinct speed advantage. Both methods show the evident residual around 22 A suggesting the presence of a CX emission component from the interaction of hot and cold gas.