Repository for developing H->gg analyses, starting from (skimmed) nanoAOD inputs. Will contain machinery for the standard tasks in developing an H->gg analysis:
- Looping over nanoAOD and making yield tables + data/MC plots
- MVA training: MVA input file prep, tools for BDT/DNN training, writing output to an ntuple for SR optimization
- Signal region optimization: optimize cut(s) on MVA(s) to maximize expected sensitivity to some observable
- For first time setup, run
source setup.shto create a virtual environment for python3 and install all necessary packages. - After the first time, you can activate your virtual environment with
source env/bin/activate
- Keep track of to-do's, problems, and planned developments in the Issues tab
- For major revisions/additions, make a pull request
- For minor changes/bug fixes, commit directly to
main - Try to somewhat loosely adhere to PEP 8 style guidlines (or at least make your code readable and add comments)
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