HIR3X Helmholtz International Laboratory on Reliability, Repetition, Results at the most Advanced X-Ray Sources

Project Title
HIR3X Helmholtz International Laboratory on Reliability, Repetition, Results at the most Advanced X-Ray Sources
Principal Investigator
Analytical methods based on radiation produced by particle accelerators play a key role in many fields of science. Their intense radiation that is tunable over a wide wavelength range in the X-ray regime allows for the determination of the atomic and element specific electronic structure and dynamics of matter not only of bulk materials but also for extremely small samples or surface layers of less than one atomic layer. Synchrotron radiation sources of the newest generation and the envisaged upcoming generation will boost the limit even further in the study of even smaller sample regions and non-crystalline samples.
While storage-ring-based sources are able to access the static structure and slow dynamics in materials that are characteristic for e.g. the movement of larger atomic groups during a molecular reaction, Free Electron Lasers (FELs) opened up a totally new field in ultrafast science to capture fast atomic movements and electronic changes on the relevant time scales of several femtoseconds, and enabling totally new experiments by their extremely high single pulse intensities like single-shot imaging.
Both institutions, SLAC and DESY, are pioneers at the forefront of FEL research. The FLASH facility at DESY was the first FEL in the VUV and soft X-ray regime, which started user operation in 2005 to mainly study ultrafast electronic properties and processes in matter and imaging at the nanometer scale. The LCLS at SLAC was the first FEL to operate in the harder X-ray regime, which brought such studies of the dynamics and structure of matter to atomic distances. FELs can be categorized in two main classes: 1) FELs based on normal conducting linear accelerators like LCLS, FERMI@ELLETTRA, SACLA@SPRING-8, PAL-FEL and SwissFEL@PSI with pulse repetition rates in the 30-120 Hz regime and 2) FELs based on superconducting linac technology like FLASH, the European XFEL and the upcoming LCLS II (now under construction) that are able to deliver pulse repetition rates in the 5 – 1000 kHz range. The latter facilities allow several FEL undulators to be served by one linac and thus have dramatically increased throughput. Together with their extremely high average brightness produced by the high pulse repetitions rates, this allows rapid measurement of large datasets that are required to investigate extremely dilute targets like gas phase reactions or capture rare events such as the transition state in a chemical reaction.
The optimum operation of a FEL is still far from routine due to the immense complexity of these installations. Changes of beam parameters can be time consuming even for expert operators. However, to fully exploit the capabilities of these FEL sources not only the operation of the accelerator facilities is a major challenge but also the transport of the photon beam, the handling and ultrafast delivery of samples as well as coping with the huge amounts of data that these facilities produce. Especially in the latter case, quick online evaluations of the measured data are desperately needed to steer the course of the experiments.
Since these challenges are similar for LCLS and LCLS II at SLAC and for FLASH and European XFEL in Hamburg, SLAC and DESY have already a long tradition of collaboration on various levels in the field of accelerators, FEL physics, and photon science. Both institutions are key players in the field in the world. Based on the existing collaboration SLAC and DESY want to tackle jointly the challenges mentioned above, which are growing more urgent with the rise of high repetition rate FELs. In addition to a closer collaboration in search for sustainable technical solutions with respect to beam transport and sample delivery one of the main collaboration tasks will be the further development of artificial-intelligence-based techniques for the control of the FEL facilities that require ten thousand to thirty thousand control parameters to be adjusted for optimum and reliable systems operation. Artificial intelligence (AI) and machine learning algorithms will also e developed for the quasi real-time assessment of data quality and first evaluation. The hope is that by utilizing suitable AI software vetoes or triggers it will be possible to effectively reduce the data volume to a sustainable and meaningful level.
All these measures are intended to stabilize and optimize the operation of high repetition rate FELs and the corresponding experimental stations and as such to make the analytical techniques that they make available to a larger community of non-expert users.