Systematics module

This module contains the systematics functions for the FAIR Challenge. Originally written by David Rousseau, and Victor Estrade.

systematics.all_bkg_weight_norm(weights, label, systBkgNorm)

Apply a scaling to the weight.

Args:

weights (array-like): The weights to be scaled label (array-like): The labels systBkgNorm (float): The scaling factor

Returns:

array-like: The scaled weights

systematics.diboson_bkg_weight_norm(weights, detailedlabel, systBkgNorm)

Apply a scaling to the weight. For Diboson background

Args:

  • weights (array-like): The weights to be scaled

  • detailedlabel (array-like): The detailed labels

  • systBkgNorm (float): The scaling factor

Returns:

array-like: The scaled weights

systematics.get_bootstrapped_dataset(test_set, mu=1.0, seed=31415, ttbar_scale=None, diboson_scale=None, bkg_scale=None, poisson=True)

Generate a bootstrapped dataset

Args:

  • test_set (dict): The original test dataset

  • mu (float): The scaling factor for htautau background

  • seed (int): The random seed

  • ttbar_scale (float): The scaling factor for ttbar background

  • diboson_scale (float): The scaling factor for diboson background

  • bkg_scale (float): The scaling factor for other backgrounds

Returns:

pandas.DataFrame: The bootstrapped dataset

systematics.get_systematics_dataset(data, tes=1.0, jes=1.0, soft_met=0.0)
systematics.make_unweighted_set(data_set)
systematics.mom4_manipulate(data, systTauEnergyScale, systJetEnergyScale, soft_met, seed=31415)

Manipulate primary inputs : the PRI_had_pt PRI_jet_leading_pt PRI_jet_subleading_pt and recompute the others values accordingly.

Args:

  • data (pandas.DataFrame): The dataset to be manipulated

  • systTauEnergyScale (float): The factor applied to PRI_had_pt

  • systJetEnergyScale (float): The factor applied to all jet pt

  • soft_met (float): The additional soft MET energy

  • seed (int): The random seed

Returns:

pandas.DataFrame: The manipulated dataset

systematics.postprocess(data)

Select the events with the following conditions: * PRI_had_pt > 26 * PRI_jet_leading_pt > 26 * PRI_jet_subleading_pt > 26 * PRI_lep_pt > 20

This is applied to the dataset after the systematics are applied

Args:

data (pandas.DataFrame): The manipulated dataset

Returns:

pandas.DataFrame: The postprocessed dataset

systematics.repeat_rows_by_weight(data_set, seed=31415)
systematics.systematics(data_set=None, tes=1.0, jes=1.0, soft_met=0.0, seed=31415, ttbar_scale=None, diboson_scale=None, bkg_scale=None, dopostprocess=True)

Apply systematics to the dataset

Args:

  • data_set (dict): The dataset to apply systematics to

  • tes (float): The factor applied to PRI_had_pt

  • jes (float): The factor applied to all jet pt

  • soft_met (float): The additional soft MET energy

  • seed (int): The random seed

  • ttbar_scale (float): The scaling factor for ttbar background

  • diboson_scale (float): The scaling factor for diboson background

  • bkg_scale (float): The scaling factor for other backgrounds

Returns:

dict: The dataset with applied systematics

systematics.ttbar_bkg_weight_norm(weights, detailedlabel, systBkgNorm)

Apply a scaling to the weight. For ttbar background

Args:

  • weights (array-like): The weights to be scaled

  • detailedlabel (array-like): The detailed labels

  • systBkgNorm (float): The scaling factor

Returns:

array-like: The scaled weights