| name | root-data-analyzer |
| description | ROOT/CERN data analysis skill for high-energy physics data processing, histogramming, and statistical analysis |
| allowed-tools | ["Bash","Read","Write","Edit","Glob","Grep"] |
| metadata | {"specialization":"physics","domain":"science","category":"particle-physics","phase":6} |
| graph | {"domains":["domain:physics"],"skillAreas":["skill-area:statistical-analysis","skill-area:mathematical-reasoning","skill-area:data-analysis"],"workflows":["workflow:experiment-design","workflow:peer-review-cycle"],"roles":["role:research-scientist","role:computational-scientist"]} |
ROOT Data Analyzer
Purpose
Provides expert guidance on ROOT data analysis for high-energy physics, including TTree manipulation, histogram fitting, and statistical modeling with RooFit.
Capabilities
- TTree/TChain manipulation
- Histogram creation and fitting
- RooFit statistical modeling
- TCanvas visualization
- ROOT macro development
- PyROOT integration
Usage Guidelines
- Data Access: Use TTree and TChain for efficient data access
- Histogramming: Create and fill histograms with proper binning
- Fitting: Use RooFit for advanced statistical modeling
- Visualization: Create publication-quality plots with TCanvas
- Python Integration: Use PyROOT for Python-based analysis
Tools/Libraries
- ROOT
- RooFit
- RooStats
- uproot