Welcome to tea

Toolkit for Efficient Analysis logo

tea—the Toolkit for Efficient Analysis—is a C++ and Python toolkit for loop-based analysis of ROOT trees. It handles repetitive work such as tree I/O, object collections, event selection, histogramming, plotting, corrections, and batch submission while leaving analysis decisions in readable code and Python configuration.

New users should start with the guided LLP tutorial. For a shorter check that the installation works, follow First analysis.

Main workflow

  1. Install tea inside an analysis repository.
  2. Read ROOT trees and define object collections.
  3. Select or skim events.
  4. Produce histograms and make plots.
  5. Submit independent jobs when the input grows.

Get help

For questions, bugs, and feature requests, open a GitHub issue.

Analyses using tea

tea has supported collider and phenomenology analyses using NanoAOD-like and HepMC-derived ROOT inputs. The analyses page keeps this community list separate from the beginner path.