Seminars Archive

High-speed and high-throughput X-ray tomography

Abstract
As the sophistication of today's experiments grow at synchrotron light sources, collecting the most informative data has become greatly relevant, necessitating the development of methods and techniques that can provide good quality reconstructions from big data streams of high-throughput scanners. Overcoming these challenges commonly requires developing better approximations of physical systems, and when these approximations are not available or too costly to compute, approaches based on machine learning can help in filling the missing information and/or automating the process. In this talk, I will first give a broad overview of the status of imaging and microscopy applications, and then describe how existing big data, compressed sensing, and machine learning methods can be adopted to enable faster and reliable information extraction from complex measurement data. I will also highlight the need for an integration of hardware and software in building successful instruments of the future, especially after realization of the next-generation of x- ray sources providing orders of increased brilliance and coherence.