Speaker
Description
Due to the lack of phase information, determining the physical parameters of multilayer thin films from measured neutron and X-ray reflectivity curves is, on a fundamental level, an underdetermined inverse problem. This so-called phase problem poses limitations on standard neural networks, constraining the range and number of considered parameters in previous machine learning solutions. To overcome this, we present an approach that utilizes prior knowledge to regularize the training process over larger parameter spaces. [1-3] We demonstrate the effectiveness of our method in various scenarios, including multilayer structures with box model parameterization and a physics-inspired special parameterization of the scattering length density profile for a multilayer structure. By leveraging the input of prior knowledge, we can improve the training dynamics and address the underdetermined ("ill-posed") nature of the problem.
[1] V. Munteanu, V. Starostin, A. Greco, L. Pithan, A. Gerlach, A. Hinderhofer, S. Kowarik, and F. Schreiber.
Neural network analysis of neutron and X-ray reflectivity data incorporating prior knowledge
J. Appl. Cryst. 57 (2024) 456
[2] V. Starostin, M. Dax, A. Gerlach, A. Hinderhofer, Á. Tejero-Cantero, and F. Schreiber.
Fast and reliable probabilistic reflectometry inversion with prior-amortized neural posterior estimation
Sci. Adv. (2025), in print
[3] L. Pithan, V. Starostin, D. Marecek, L. Petersdorf, C. Völter, V. Munteanu, M. Jankowski, O. Konovalov, A. Gerlach, A. Hinderhofer, B. Murphy, S. Kowarik, and F. Schreiber.
Closing the loop: Autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments
J. Synchrotron Rad. 30 (2023) 1064
