The Shanghai soft X-ray Free-Electron Laser facility (SXFEL) is being developed in two steps; the SXFEL test facility (SXFEL-TF), and the SXFEL user facility (SXFEL-UF). The SXFEL-TF is a critical development step towards the construction a soft X-ray FEL user facility in China, and is under commissioning at the Shanghai Synchrotron Radiation Facility (SSRF) campus. The test facility is going to generate 8.8 nm FEL radiation using an 840 MeV electron linac passing through the two-stage cascaded HGHG-HGHG or EEHG-HGHG (high-gain harmonic generation, echo-enabled harmonic generation) scheme. The construction of the SXFEL-TF started at the end of 2014. Its accelerator tunnel and klystron gallery were ready for equipment installation in April 2016, and the installation of the SXFEL-TF linac and radiator undulators were completed by the end of 2016. In the meantime, the SXFEL-UF, with a designated wavelength in the water window region, began construction in November 2016. This was based on upgrading the linac energy to 1.5 GeV, and the building of a second undulator line and five experimental end-stations. Construction status and the future plans of the SXFEL are reported in this paper.

Bunch-by-bunch beam parameters monitor is a very useful tool for accelerator operation and optimization. An in situ bunch-by-bunch three-dimensional position and charge measurement system has been developed at the Shanghai Synchrotron Radiation Facility. Signals from beam position monitors carry almost full beam information, a broadband digital receiver can retain most information. Therefore, a four-channel wideband oscilloscope is employed to capture the signals from all electrodes of the beam position monitor. Correlation coefficient-based methods are used to extract these bunch-by-bunch parameters synchronously. We evaluated the performance of this bunch-by-bunch information extraction method, the measurement uncertainty of longitudinal phase is less than 0.2 ps, the uncertainty of transverse position is less than $10\mu \mathrm{m}$ and the charge uncertainty is 0.3% under the condition that the bunch charge is 600 pC. Thanks to the great performance of the new system, it is accurate enough to study the transient state during injection. In addition, an in situ wakefield measurement system is planned to be established based on this system.

We report on the application of machine learning (ML) methods to extract longitudinal phase information such as parameters of the synchrotron damping oscillation. Parameters of the synchrotron damping oscillation are important for the evaluation of machine status and bunch stability. It is of concern to extract these parameters with high-speed and high-precision. The previous methods, such as multiparameter nonlinear fitting and table look-up, are slower and easily fall into local optimal solutions. Our approach based on ML-image processing consists of training a virtual diagnostic to predict parameters using the beam position monitor (BPM) electrical signal data as inputs. We find that when the noise of data is large, our ML-model can still get better results than other methods, an important step toward on-line multiparameter extraction from multidimensional raw data.

Wakefields and beam instabilities are important issues for a storage ring and are described by various theoretical formalisms. Direct measurements of the beam motion related to different dynamical mechanisms are a useful input to accelerator optimization. This paper reports on an experimental method based on a simplified wakefield model, where bunch-by-bunch position information were monitored during the transient injection process at the Shanghai Synchrotron Radiation Facility. Processing the bunch-by-bunch data by means of the principal component analysis allowed for immediate operational improvements—such as in situ compensation of the kicker leakage, and energy matching between the booster and the storage ring.