Speaker
Description
The China Spallation Neutron Source Rapid Cycling Synchrotron (CSNS-RCS) operates with a demanding acceleration cycle and tight beam-loss constraints, which puts high requirements on its operation tools. We present a set of operator applications developed on Pyapas, the Python-based accelerator physics application framework used at CSNS, HEPS and BEPCII. These applications talk to the EPICS control system through Channel Access and reuse the framework's common building blocks for GUI, data acquisition and online accelerator modelling with Ocelot.
Five applications are highlighted:
RCS Online Tuner (RCDS). A Python port of the Robust Conjugate Direction Search algorithm for online, noise-tolerant optimization of orbit corrector settings, taking the total beam loss (BLM) or the transmission efficiency as the objective. A virtual-accelerator mock mode allows safe development and training without an IOC.
RCS Local Bump. Computes 41-point field-increment waveforms for up to 37 correctors, following the measured Bρ(t) ramp from injection to extraction. The waveforms are superposed onto the existing EPICS AC waveform channels and verified by readback after each write, supporting the creation of stable local bumps for injection and extraction studies.
MIA/ICA. A turn-by-turn BPM analysis tool that parses TBT data files, applies Second-Order Blind Identification (ICA) to separate coupled betatron modes, and overlays the measured tunes on the resonance diagram. Online connection to the Libra turn-by-turn PVs is being added.
RCS BLM Display. A beam-loss-monitor display showing BLM location photos and waveform statistics (Max/Mean/Std) over the R1–R4 sectors, providing quick identification of loss locations during commissioning and operation.
RCS Parameters Display. A machine-parameter monitor covering 32 BPMs, 82 BLMs and the transmission efficiency, with Libra turn-by-turn BPM waveforms. A 500-shot ring buffer provides real-time display, front/back-window comparison and storage in CSV/HDF5 formats, with automatic alerts when the transmission efficiency drops.
These tools demonstrate how a lightweight Python/EPICS framework can turn machine data into physics-based, interactive operation aids, and how advanced optimization and analysis techniques can be introduced into daily accelerator operation with low development cost.
