Speaker
Danilo Ferreira De Lima
(Ruprecht Karls Universitaet Heidelberg (DE))
Description
Data analysis pipelines typically require experiment- or data-dependent parameters to be tuned. Serial Femtosecond Crystallography (SFX) is a powerful technique that allow to disclose structural information in a time-resolved fashion. We present here an attempt of tuning the parameters of a software tool widely used for SFX data analysis, CrystFEL, using a model-free actor-critic Reinforcement Learning method to automatically tune the input parameters in SFX for fast online feedback, allowing users to adapt their experiments to maximize data quality and output on-the-fly.
