Description

CERN’s experiments are committed to publishing their data in a form that is accessible to all, both for research purposes and for education. For example, the ATLAS experiment provides Jupyter notebook exercises based on live-analysing reduced forms of the real collider data.

But particle-physics researchers also use simulations of data as a crucial tool for testing theories and for understanding the background processes that new physics effects have to be isolated from. For this we use Monte Carlo (MC) event-generator codes, which are statistical implementations of the fundamental physics theory that sample real-looking events from the predicted particle types and kinematics. These are not yet represented in open-data exercises.

Task ideas

In this project we will develop new tools and exercises for extending open-data analysis resources to include MC event simulations. It will both reduce the entry barriers to outreach with open data and enable more engaging exercises with hypothetical new-physics models.

Expected results and milestones

• Develop a library of wrapper functions to make open-data analysis more approachable for non-experts.
• Create functions and datasets for loading and analysing MC event samples through Jupyter.
• Develop a new Jupyter+Binder worksheet for outreach-oriented open-data MC analysis.

Requirements

• Python
• Jupyter
• Binder
• Gitlab CI
• git

Links

• ATLAS open data
• Jupyter
• Binder

Mentors

• Andy Buckley - CERN
• Chris Gutschow - CERN

Additional Information

• Difficulty level (low / medium / high): medium
• Duration: 175 hours
• Mentor availability: June-October

Corresponding Project

• MCnet

Participating Organizations

• UofGlasgow
• UCLondon

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