Autonomous Modeling of High-Resolution X-ray Spectra Using Robust Global Optimization Methods
High-energy astrophysics is a young, fast-growing field that has opened a new window to the invisible universe. X-ray spectroscopy is a particularly powerful tool to discover the nature of highenergy sources, since their physical properties are inferred by comparing physical models to observations. Therefore, methods for fitting models to data are of crucial importance. The standard approach is via local optimization. However, these methods suffer from a number of limitations. At the U. of Manitoba, we are developing software based on evolutionary algorithms targeted to perform global optimization for X-ray spectroscopy. These advanced methods will be particularly instrumental for multi-parameter models and high-fidelity X-ray data. The project is also timely with the upcoming (early 2016) launch of the X-ray mission, ASTRO-H, led by Japan partnering with NASA, ESA and the Canadian Space Agency, and with the U. of Manitoba as an active member. This project will place the Manitoba in a leadership role by providing an innovative software package for the X-ray astronomy community, and is directly relevant to nQube Technical Computing, dramatically illustrating the capabilities of their optimization methods on challenging and high-profile model fitting problems.