Bayesian experimental design and parameter estimation for ultrafast spin dynamics

Advanced experimental measurements are crucial for driving theoretical developments and unveiling novel phenomena in condensed matter and materials physics, which often suffer from the scarcity of large-scale facility resources, such as x-ray or neutron scattering centers. To address these limitations, we introduce a methodology that leverages the Bayesian optimal experimental design paradigm to efficiently uncover key quantum spin fluctuation parameters from x-ray photon fluctuation spectroscopy (XPFS) data. Our method is compatible with existing theoretical simulation pipelines and can also be used in combination with fast machine learning surrogate models in the event that real-time simulations are unfeasible. Our numerical benchmarks demonstrate the superior performance in predicting model parameters and in delivering more informative measurements within limited experimental time. Our method can be adapted to many different types of experiments beyond XPFS and spin fluctuation studies, facilitating more efficient data collection and accelerating scientific discoveries.

Automated summary

Advanced experimental measurements play a crucial role in driving theoretical developments and uncovering novel phenomena in condensed matter and materials physics. However, the scarcity of large-scale facility resources often poses limitations. In this study, we introduce a methodology that utilizes the Bayesian optimal experimental design paradigm to efficiently extract key quantum spin fluctuation parameters from XPFS data. Our method is compatible with existing theoretical simulation pipelines and can also be combined with fast machine learning surrogate models to accelerate data collection and scientific discoveries.