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- Monte Carlo simulation tools for X-ray imaging and fluorescence
Monte Carlo simulation tools for X-ray imaging and fluorescence
ESRF Auditorium, Grenoble - France
24 and 25 February 2014
Scientific committee:
Bruno Golosio, Tom Schoonjans, Piernicola Oliva, Laszlo Vincze and Claudio Ferrero
Programme here
The number of applications of the Monte Carlo method in the fields of X-ray imaging and fluorescence has continuously increased over the past two decades, as witnessed by the breathtaking growth of related publications. Developers of Monte Carlo based algorithms/codes as well as their users have produced pioneering results for the prediction of experimental outcomes, the characterization of setups, for dosimetry or even quantification purposes.
During this two-day workshop, the eminent invited speakers below and contributors will disseminate their work through a series of presentations:
- Mateusz Czyzycki (AGH University of Science & Technology, Krakow, and DESY Photon Science, Hamburg)
- Viviana Scot (University of Bologna)
- Antonio Brunetti (University of Sassari)
- Charalampos Zarkadas (PANalytical)
These presentation sessions will be alternated with hands-on tutorials that will cover three open source packages that are actively being developed by the organizers:
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xraylib: a full-fledged library providing convenient access to a large number of physical datasets that are essential in the field of photon--matter interactions. The core library is implemented in C, however it features bindings for a dozen of other languages.
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xrmc: a Monte Carlo method based tool allowing for the simulation of a wide range of X-ray imaging and spectroscopy experiments (absorption, phase contrast, fluorescence, mammography, ...), with support for complex geometry and sample description.
- XMI-MSIM: a complete package aiming to simulate energy-dispersive x-ray fluorescence experiments and designed from the ground up bearing ease of use, speed and accuracy in mind. It serves also as a plug-in to PyMca where it can be used for quantitative analysis of datasets.