论文标题
与归一化流的GISAXS数据的摊销贝叶斯推断
Amortized Bayesian Inference of GISAXS Data with Normalizing Flows
论文作者
论文摘要
放牧的小角度X射线散射（GISAXS）是一种用于研究纳米级材料的现代成像技术。对象的参数的重建会引发一个不当的反问题，当仅提供平面内GISAXS信号时，该问题更加复杂。传统上使用的推理算法（例如近似贝叶斯计算（ABC））依赖于计算昂贵的散射仿真软件，使分析高度耗时。我们提出了一个基于模拟的框架，该框架结合了变异自动编码器和标准化流，以估计给定GISAXS数据的对象参数的后验分布。我们将推理管道应用于实验数据，并证明我们的方法可以通过数量级来降低推理成本，同时与ABC产生一致的结果。
Grazing-Incidence Small-Angle X-ray Scattering (GISAXS) is a modern imaging technique used in material research to study nanoscale materials. Reconstruction of the parameters of an imaged object imposes an ill-posed inverse problem that is further complicated when only an in-plane GISAXS signal is available. Traditionally used inference algorithms such as Approximate Bayesian Computation (ABC) rely on computationally expensive scattering simulation software, rendering analysis highly time-consuming. We propose a simulation-based framework that combines variational auto-encoders and normalizing flows to estimate the posterior distribution of object parameters given its GISAXS data. We apply the inference pipeline to experimental data and demonstrate that our method reduces the inference cost by orders of magnitude while producing consistent results with ABC.
