cms-sas-utils 0.2

Some tools for SaS derivation from HDNA files reader to plotting

SaS utils

Package to convert file to IJazZ format, combine correction files and apply correction to parquet

HiggsDNA Reader

This script, reader_higgsdna.py, is designed to read and convert HiggsDNA parquet files into the ijazz_2p0 format. Below is a brief description of its main functions:

Functions

main()

• Description: Entry point of the script. It reads the configuration file and calls the read_and_convert function with the appropriate parameters.
• Usage: sas_reader_higgsdna config.yaml

read_and_convert()

• Description: Reads and converts the HiggsDNA parquet file to the ijazz_2p0 format.
• Parameters:
  • data_dict (dict): Dictionary containing data file information.
  • mc_dict (dict): Dictionary containing Monte Carlo (MC) file information.
  • dir_out (str): Output directory.
  • stem_out (str): Stem of the output file.
  • is_ele (bool): Flag to use GSF electron energy.
  • mc_weight (str): Name of the column containing the MC weights.
  • corrlib_scale (dict): Correction library to correct the energy scale in data.
  • corrlib_smear (dict): Correction library to smear the MC.
  • add_vars (List): Additional variables to include.
  • charge (int): Charge selection.
  • selection (str): Selection criteria.
  • reweight_selection (str): Reweighting selection criteria.
  • pileup_reweighting (bool): Flag to apply pileup reweighting.
  • do_reweight (bool): Flag to apply reweighting.
  • subyear (str): Subyear information.
  • backgrounds (list): List of background processes.
  • year (str): Year information.
  • save_dt (bool): Flag to save data.
  • save_mc (bool): Flag to save MC.

Normalisation

• Description: Contains cross-section (XS) and luminosity (Lumi) values for different processes and years.

MC weights

• HiggsDNA: 3 weights are saved:

  • genWeight: NLO weights from the generator
  • weight = genWeight * weight_central: NLO weights + reweight from HDNA
  • weight_central: reweight from HDNA ie Pileup,..

• Output of the reader:

  • genWeight: NLO weights from the generator.
  • weight = genWeight * weight_central * norm * RW: NLO weights with HDNA and reader reweighting and normalization.
  • genWeight_normed = genWeight * norm: NLO weights from the generator normalized to the XS and luminosity.
  • weight_central = weight_central * RW: LO weights with HDNA and reader reweighting.

Columns

• Description: Defines the columns for photons and electrons.

Filters

• Description: Defines the filters for photons, electrons, and events.

Usage Example

sas_reader_higgsdna config/cms/reader_higgsdna_example.yaml

This script is essential for converting and normalizing HiggsDNA data for further analysis in the ijazz_2p0 framework using this example reader_higgsdna_example.yaml.

Combine corrlib

combine_csets():

• Description: Combine different corrlib correction files, some files could use always the nominal scale for variations (if only one variations should be considered to avoid double counting).
• Parameters:
  • cset_files (List[Union[str, Path]]): list of corrlib files
  • icset_fix_scale (Union[List,Tuple]): list of corrlib for which the nominal scale only should be use
  • dir_results (Union[str, Path]): directory
  • dset_name (str, optional): identfier of the datase. Defaults to 'DSET'.
  • cset_version (int, optional): version of the set of corrections. Defaults to 1.

Usage Example

Combining the 6 steps:

file_corr0=TimeDep/EGMScalesSmearing_2022preEE.v1.json.gz
file_corr1=EtaR9/EGMScalesSmearing_2022preEE.v1.json.gz
file_corr2=FineEtaR9/EGMScalesSmearing_2022.v1.json.gz
file_corr3=PT/EGMScalesSmearing_2022.v1.json.gz
file_corr4=Gain/EGMScalesSmearing_2022.v1.json.gz
file_corr5=PTsplit/EGMScalesSmearing_2022preEE.v1.json.gz

    ijazz_corrlib_combine $file_corr0 $file_corr1 $file_corr2 $file_corr3 $file_corr4 $file_corr5 -i 1 2 3 4  -v 1 -o . -d Pho_2022preEE

Create EGMScalesSmearing_Pho_2022preEE.v1.json.gz output file. Including a compound correction for scales EGMScale_Compound_Pho_2022preEE and for each step, the scale correction :EGMScale_Pho{step_name}_2022preEE and smearing correction:EGMSmearAndSyst_Pho{step_name}_2022preEE.

We use -i 1 2 3 4 because the systematics are computed in the last step (step5) and the time dependent correction does not include escale then we fix the escale for the file 1, 2, 3 and 4. Then scale = scale0 * scale1 * scale2 * scale3 * scale4 * scale5 but escale = escale5

Correct file

Apply Scale and Smearing to parquet files using this example validation_samples.yaml. Where we apply the compound scale on data and the smearing compute in the last step on MC.

sas_file_corrector config/cms/validation_samples.yaml