2026ApJ...998..297M

The standard approach to modeling X-ray spectral data relies on local optimization methods, such as the Levenberg-Marquardt algorithm. While effective for simple models and speedy spectral fitting, these local optimizers are prone to becoming trapped in local minima, particularly in high-dimensional or degenerate parameter spaces, and typically require extensive user intervention. In this work, we introduce XFit, a global optimization method for fitting X-ray data, which makes extensive use of the Ferret evolutionary algorithm. XFit enables automated exploration of complex parameter spaces, efficient mapping of confidence intervals, and identification of degenerate solutions that may be overlooked by local methods. We demonstrate the performance of XFit using two representative X-ray sources: the central compact object in Cassiopeia A and the supernova remnant G41.1-0.3. These examples span both low- and high-dimensional models, allowing us to illustrate the advantages of global optimization. In both cases, XFit produces solutions that are consistent with or improve upon those found with traditional methods, while also revealing alternative fits or degenerate solutions within statistically acceptable confidence levels. The automated mapping of parameter space offered by XFit makes it a powerful complement to existing spectral-fitting tools, particularly as models and data quality become increasingly complex. Future work will expand the application of XFit to broader datasets and more physically motivated models.