[TOC]

Fitter

This project is meant to automate:

• Building of models
• Fits to MC
• Fits to data
• Creation of validation plots, tables, etc
• Application of constraints
• Obtention of fitting information, specially:
  • Mass scales, resolutions
  • Signal yields for resonant and rare modes

And be used as an input for:

• Systematics evaluation
• Extraction of $R_K$ and $R_K^*$
• Calibrations

Ideally with minimal changes for new observables. The class is organized as in the diagram below:

This project makes heavy use of caching through the Cache class and thus:

• It allows for fast reruns
• It allows for the fitters to also be used to retrieve parameters

Usage

For fits to data do:

fit_rx_data -c rare/electron -q central -C 0.5 -P 0.5

to use the configuration in src/fitter_data/rare/electron/data.yaml with an MVA working point of 0.5 for the combinatorial and part reco BDTs.

This should:

• Apply the selection on the MC and data
• Fit the MC
• Use the MC to create components for the fit to the data
• Fit the data and save everything

To do the same from a python script do:

from dmu.generic        import utilities  as gut
from rx_selection       import selection  as sel
from fitter.data_fitter import DataFitter

cfg = gut.load_conf(
    package='fitter_data',
    fpath  ='rare/electron/data.yaml')

with sel.custom_selection(d_sel={
        'nobr0' : 'nbrem != 0',
        'bdt'   : 'mva_cmb > 0.60 && mva_prc > 0.40'}):
    ftr = DataFitter(
        sample = 'DATA_24_*',
        trigger= 'Hlt2RD_BuToKpEE_MVA',
        project= 'rx',
        q2bin  = 'central',
        cfg    = cfg)
    obj = ftr.run()

where obj is a DictConf (from the omegaconf project) dictionary where the parameters and errors can be accessed as:

val = obj['name']['value']
err = obj['name']['error']