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PyECLOUD is a newly developed code for the simulation of the electron cloud (EC) build-up in particle accelerators. Almost entirely written in Python, it is mostly based on the physical models already used in the ECLOUD code but, thanks to the implementation of new optimized algorithms, it exhibits a significantly improved performance in accuracy, speed, reliability and flexibility. PyECLOUD simulations have been already broadly employed for benchmarking the EC observations in the Large Hadron Collider (LHC). Thanks to the new feature of running EC simulations with bunch-by-bunch length and intensity data from machine measurements, the “scrubbing” process of the LHC beam pipes could be reconstructed from heat load measurements in the cryogenic dipoles. In addition, PyECLOUD simulations also provide the estimation of the bunch-by-bunch energy loss, which can be compared with the measurements of the stable phase shift. They can also provide the correct EC distribution data for beam dynamics simulations with the HEADTAIL code.
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