Antonin Sulc, website : sulcantonin.github.io
This is a website which was put on for purpose to share our materials for the tutorial Tutorial on Anomaly Detection at 3rd ICFA Beam Dynamics Mini-Workshop on Machine Learning Applications for Particle Accelerators (https://www.bnl.gov/mlaworkshop2022/)
(ipynb basics) (ipynb autoencoder) (ipynb one class loss) (ipynb bce) (ipynb semi-supervised anomaly loss) (ipynb anogan)
If you do not want to install Jupyter or run e.g. Google Colab, you can simply execute the notebook via (https://mybinder.org/).
Or you can use following link which was prepared for us (thank you very much Jon E.) https://www.sirepo.com/jupyterhublogin
Code + Data will follow soon!
IPAC'22 Conference Contribution https://github.com/sulcantonin/BPM_IPAC22
An accurate assessment of beam orbits is a reliable characteristic of operations at SASE beamlines at European XFEL. The current availability of hardware for data-driven models like GPUs enabled to deploy models which are capable of handling enormous throughput of data provide tools for more sophisticated analysis of current operations with state-of-the-art machine learning models. In this work, we are showing our current progress of beam orbit analysis at European XFEL with pure model-free data-driven tools. We examine more advanced models which take the intra-bunching properties into consideration and analyze beam operations with various anomaly detection methods.