FPGA4XPCS: Efficient Real-Time Computation of Autocorrelation Functions for X-Ray Photon Correlation Spectroscopy using FPGAs
Overview
Dynamical processes in condensed matter are of importance in various scientific disciplines. X-ray photon correlation spectroscopy (XPCS) is a coherent synchrotron X-ray scattering technique that allows measurements of dynamical phenomena in condensed matter over a wide range of length and time scales. The length scales currently accessible with XPCS range from the atomic scale to the mesoscale; the corresponding time scales range from milliseconds to minutes. For low-emittance storage rings (e.g., PETRA IV and ESRF-EBS), the signal-to-noise ratio of XPCS experiments increases linearly with brilliance, allowing access to faster time scales. Already today, many XPCS experiments are routinely performed at the high frame rates of area detectors, which generate a large stream of unprocessed data. This results in a significant mismatch between data processing and measurement time, which reduces the efficiency of many XPCS experiments. In other words, data reduction, i.e., the computation of autocorrelation functions from time series of detector images, takes orders of magnitude longer than the actual data acquisition. However, timely access to the reduced data is essential to efficiently control and steer XPCS experiments. This is especially important when exploring the experimental parameters to determine the correct time window for dynamics. Nowadays, during the experiment, this is often difficult to perform in a controlled manner due to the long dead times required for data reduction. Therefore, the goal of this proposal is to develop methods for real-time data reduction for XPCS using FPGAs. We anticipate that our project will enable novel scientific applications at synchrotron facilities in this way, thus ensuring the scientific excellence of these facilities.
Key Facts
- Grant Number:
- 05K22PP2
- Research profile area:
- Optoelectronics and Photonics
- Project type:
- Research
- Project duration:
- 10/2022 - 09/2025
- Funded by:
- BMBF