ijazz 0.1.1

I Just AnaliZe the Z 2.0

IJazZ_2p0

This package includes the fitter part only. To use the full workflow, use law_ijazz.

Documentation

The package documentation is available https://ijazz.readthedocs.io/en/latest/

Install the package

Create conda env

conda create -n ijazz python=3.9
conda activate ijazz

Install package in editable mode

Clone the repo

git clone https://gitlab.cern.ch/fcouderc/ijazz_2p0.git
cd ijazz_2p0

Install package in editable mode

pip install -e .

Install from pypi

pip install ijazz

Derive Scale and Smearing

ijazz_sas config/sas_config.yaml

where the config file is config/sas_config.yaml :

file_dt: data/cms/2022/higgs_dna_2022PreEE.pho.data.TimeCorr.parquet
file_mc: data/cms/2022/higgs_dna_2022PreEE.pho.mc.parquet
dir_results: results/test/
cset_name: "EtaR9"  # name of the correction (appended to the file name if correction
cat_latex: # latex names for parameters plots
  ScEta: "SuperCluster $\eta$"
  AbsScEta: "SuperCluster $|\eta|$"
  r9: "Seed Cluster R9"
  pt: "$p_T$ (GeV)"
dset_name: "TestDataSet" # dataset name
scale_flat_syst: 0.5e-3  # added in quadrature to the full list of scale systematics
smear_flat_syst: 0       # added in quadrature to the full list of smear systematics

syst: # list of systematics to be computed
  win_mll: # name of systematic
    # parameters to be overwritten
    fitter:
      win_z_mc: [65, 115]
      win_z_dt: [70, 110]
  cut_variation: # name of systematic
    # parameters to be overwritten
    sas:
      cut: pt1 > 30 and pt2 > 30
corrlib:
  cset_description: "EM object scale and smearing vs eta / r9"
  cset_version: 1
  
sas:
  use_rpt: true         # - when categories include pt, fit with the relative pt 
  hess: numerical       # - hessian matrix: null, numerical, analytical (not advised)
  learning_rate: 1.0e-3 # - learning rate to the keras optimizer 
  name_pt_var: pt       # - name of the pt variable in case used in categorisation and to be corrected
  err_mc: true          # - compute the uncertainty due to limited MC statistics
  correct_data: true    # - correct the data
  correct_mc: true      # - smear the MC
  categories:
      ScEta: [-3.0, -2.0, -1.49, -1.0, 0.0, 1.0, 1.49, 2.0, 3.0]
      r9: [-.Inf, 0.97, .inf]
  # - cut to apply in the input dataframes
  cut: null
fitter:
  win_z_mc: [70, 110]      # - mass range of the dilepton mass to fit
  win_z_dt: [80, 100]      # - larger mass range to consider MC events
  min_nevt_region_mc: 100  # - minimum number of mc events per category
  min_nevt_region_dt: 20   # - minimum number of data events per category
  bin_width_dt: 'Q'        # - binning width for the data, 'Q' for quantile binning
  bin_width_mc: 0.1        # - binning width for the MC
  name_cat: cat            # - name of the category variable
  name_weights: null       # - name of the weights variable for MC
  name_mll: 'mass'         # - name of the di-lepton mass variable  
minimizer:
  dnll_tol: 0.01           # - tolerance for the change in -2logL to determine convergence
  max_epochs: 500          # - maximum number of epochs for optimization
  init_rand: False         # - if True, initializes variables (resp, reso) randomly.
  nepoch_print: 100        # - number of epochs to print the loss
  batch_size: 200          # - size of the batch for likelihood computation
  batch_training: True     # - use batch 
  device: GPU              # - device to use for the minimizer
  minimizer: Adam          # - optimization method, either 'Adam' or a SciPy minimizer (e.g., 'TNC').

Input ntuples

Input files must be parquet files with a column for the dilepton mass name_mll and columns for variables of each electrons var1 and var2. MC weights can be used using name_weights. For example:

• mass, weight_central, ScEta1, ScEta2, r91, r92, pt1 and pt2.

A reader to convert Higgs DNA output files to IJazZ input files is provided in the sas_utils package and used in the law_ijazz workflow.

Plot the SaS results

By default, the plots are automatically generated. However, you may want to customize some plot settings or compare two JSON files—either by overlaying their plots or by displaying the ratio between them.

Plot nominal results

ijazz_plot FineEtaR9/SAS2024_syst-Nominal.json -d r9 AbsScEta  --resp 0.98 1.03 --reso 0 0.06 -o FineEtaR9/SAS2024_syst-Nominal.jpg --latex higgsdna_cat_latex.json --ncol 2 --leg_fontsize small

where higgsdna_cat_latex.json is a dictionnary to map the variable name to a latex name for plotting:

{ 
  "ScEta": "$\\eta_{SC}$", 
  "AbsScEta": "$|\\eta_{SC}|$", 
  "r9": "SC R9", 
  "pt": "$p_T$ (GeV)", 
  "seedGain": "SC Gain"
}

The option -d r9 AbsScEta is used to define the axis order for plotting the variables. In this case, r9 will be plotted along the x-axis, while results as a function of AbsScEta will be overlaid.

This function can also be achieved in Python using the ijazz.plotting.plot_results_from_json function:

import ijazz.plotting

var_latex = { "ScEta": "$\\eta_{SC}$", "AbsScEta": "$|\\eta_{SC}|$", "r9": "Seed Cl. R9", "pt": "$p_T$ (GeV)", "seedGain": "Seed Cl. Gain"}

figs, ax = ijazz.plotting.plot_results_from_json('Pho/FineEtaR9/SAS2024_syst-Nominal.json', resp_range=[0.98,1.03], reso_range=[0.0,0.06], cat_latex=var_latex, dim=['r9', 'AbsScEta'], leg_fontsize='small', leg_ncols=2)

Compare SaS JSON Files

You can compare JSON files in two ways: by calculating the ratio between two files or by overlaying their plots.

Ratio Comparison

Example to plot the ratio of scale and smearing between electron regression and photon regression:

import ijazz.plotting

var_latex = { "ScEta": "$\\eta_{SC}$", "AbsScEta": "$|\\eta_{SC}|$", "r9": "Seed Cl. R9", "pt": "$p_T$ (GeV)", "seedGain": "Seed Cl. Gain"}

jsonEle = 'Ele/FineEtaR9/SAS2024_syst-Nominal.json'
jsonPho = 'Pho/FineEtaR9/SAS2024_syst-Nominal.json'

figs, ax = ijazz.plotting.plot_results_from_json({'syst': jsonEle, 'nominal': jsonPho}, jsons_mode='ratio', resp_range=[0.995,1.008], reso_range=[0.50,1.5], cat_latex=var_latex, leg_fontsize='small', leg_ncols=2)

Overlay Comparison

Example to overlay the scale and smearing results from electron regression and photon regression:

import ijazz.plotting

var_latex = { "ScEta": "$\\eta_{SC}$", "AbsScEta": "$|\\eta_{SC}|$", "r9": "Seed Cl. R9", "pt": "$p_T$ (GeV)", "seedGain": "Seed Cl. Gain"}

jsonEle = 'Ele/FineEtaR9/SAS2024_syst-Nominal.json'
jsonPho = 'Pho/FineEtaR9/SAS2024_syst-Nominal.json'

# plot scale and smearing if SaS are function of 1 or 2 variables
figs, ax = ijazz.plotting.plot_results_from_json({'Electron': jsonEle, 'Photon': jsonPho}, jsons_mode='compare', resp_range=[0.95,1.08], reso_range=[0.0,0.08], cat_latex=var_latex)

# plot scale only if SaS are function of 3 variables
figs, ax = ijazz.plotting.plot_results_from_json(
  {'Electron': jsonEle, 'Photon': jsonPho}, jsons_mode='compare'
  resp_range=[0.95,1.08], reso_range=[0.0,0.08], cat_latex=var_latex,
  param_to_plot='resp', # we need to specify which param to plot because we cannot display 4 dims
  )

# plot smearing only if SaS are function of 3 variables
figs, ax = ijazz.plotting.plot_results_from_json(
  {'Electron': jsonEle, 'Photon': jsonPho}, jsons_mode='compare',
  resp_range=[0.95,1.08], reso_range=[0.0,0.08], cat_latex=var_latex,
  param_to_plot='reso', # we need to specify which param to plot because we cannot display 4 dims
  )

NB: if the SaS are function of three variables, the second dim must be of len = 2 (e.g. low and high R9).

NB 2: it works with more than 2 input jsons: ijazz.plotting.plot_results_from_json({'2022': json22, '2023': json23, '2024': json24}, jsons_mode='compare').