Using Python to visualize and analyze ion beam experiment data at a particle accelerator facility
summary
Pipeline reads ROOT files via PyRoot into Python notebooks, uses Plotly graph objects for interactive scatter, contour and surface plots, and sends live experiment state to an ELK stack. Data is raster-scanned (typically 256x256 pixels, 10 ms dwell), real-time Rutherford backscattering tracks metal silicide formation during a 0→600 °C anneal (peaks shift and intensities change), six-hour runs produce multi-gigabyte files, and the visual tools successfully reveal depth shifts and channeling features.
AI-written summary · about
chapters (6)
- 0:00Introduction to the facility
- 3:46Physics and data collection
- 7:51Data acquisition techniques
- 13:22Analysis and visualization
- 25:09Annealing and interactivity
- 29:37Q&A session
related talks
27mTech Talk
33mIodide and Pyodide: Bringing Data Science Computation to the Web Browser
06mLightning Talk: Using Jupyter Notebooks To Gain Insight Of Your Cluster
49mExperimenting with Large Data Volumes Using Notebooks
43mAdvanced Data Visualizations In Jupyter Notebooks
42mDevelopment of Data Science Ecosystem for Ruby
16mVisualization and Analysis of Large Scale Datasets with Python
23mPython(x,y): diving into scientific Python