Built-in Modules

Common Modules

bjet_sf.py

BJetShapeSF

class analyzer.modules.common.bjet_sf.BJetShapeSF

Abstract base class for all analyzer modules. Subclasses must implement the inputs and run methods.

Configuration Variables

Name	Type	Default
input_col	Column	Required
weight_name	str	'b_tag_disc_shape'
should_run	MetadataExpr	Factory

bquarks.py

BQuarkMaker

class analyzer.modules.common.bquarks.BQuarkMaker

Select b-tagged jets from a jet collection based on DeepJet working points.

This analyzer identifies b-jets in an event by applying a threshold on the DeepJet b-tagging score as specified by central working point values.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
input_col	Column	Required
output_col	Column	Required
working_point	str	Required

categories.py

SimpleCategory

class analyzer.modules.common.categories.SimpleCategory

Create a simple numerical category from an input column.

This analyzer maps a numeric column to a category and registers it in the pipeline with associated axis information for plotting or binning. This axis will be added to all subsequent histograms.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
input_col	Column	Required
cat_name	str	Required
bins	int	Required
start	float	Required
stop	float	Required

column_tools.py

Concatenate

class analyzer.modules.common.column_tools.Concatenate

Abstract base class for all analyzer modules. Subclasses must implement the inputs and run methods.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
input_cols	list	Required
output_col	Column	Required

Count

class analyzer.modules.common.column_tools.Count

Abstract base class for all analyzer modules. Subclasses must implement the inputs and run methods.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
input_col	Column	Required
output_col	Column	Required

PromoteIndex

class analyzer.modules.common.column_tools.PromoteIndex

Promote a fixed index of a nested collection to a top-level column.

This analyzer selects a single element at a given index from each entry of a nested (jagged) input collection and stores it as a top-level column. It is commonly used to extract leading or sub-leading objects (e.g. leading jet, first lepton) from per-event collections.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
input_col	Column	Required
output_col	Column	Required
index	int	0

electrons.py

ElectronMaker

class analyzer.modules.common.electrons.ElectronMaker

Select electrons based on kinematics, cut-based ID, and isolation.

This analyzer filters electrons in an event according to minimum transverse momentum, maximum pseudorapidity, cut-based ID working point, and maximum mini-isolation.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
input_col	Column	Required
output_col	Column	Required
working_point	CutBasedWPs	Required
min_pt	float	10
max_abs_eta	float	2.4
max_mini_iso	float	0.1

event_level_corrections.py

GoldenLumi

class analyzer.modules.common.event_level_corrections.GoldenLumi

Apply a golden JSON luminosity selection for data events.

This analyzer filters events to only include those that are within certified good luminosity sections for the given data-taking era.

Configuration Variables

Name	Type	Default
selection_name	str	'golden_lumi'
should_run	MetadataExpr	Factory

L1PrefiringSF

class analyzer.modules.common.event_level_corrections.L1PrefiringSF

Apply L1 prefiring scale factors to Monte Carlo events.

This analyzer retrieves the L1 prefiring weight and adds it to a named weight column, optionally applying systematic variations.

Configuration Variables

Name	Type	Default
weight_name	str	'l1_prefiring'
should_run	MetadataExpr	Factory

NoiseFilter

class analyzer.modules.common.event_level_corrections.NoiseFilter

Apply standard noise filters to data events.

This analyzer combines multiple event-level noise flags and produces a single selection column indicating events that pass all required noise filters.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
selection_name	str	'noise_filters'

PileupSF

class analyzer.modules.common.event_level_corrections.PileupSF

Apply pileup scale factors to Monte Carlo events.

This analyzer evaluates the pileup weight based on the number of true interactions in the event and applies systematic variations if requested.

Configuration Variables

Name	Type	Default
weight_name	str	'pileup_sf'
should_run	MetadataExpr	Factory

PosNegGenWeight

class analyzer.modules.common.event_level_corrections.PosNegGenWeight

Abstract base class for all analyzer modules. Subclasses must implement the inputs and run methods.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr	Factory
weight_name	str	'pos_neg_weight'

gen_matching.py

VectorMatching

class analyzer.modules.common.gen_matching.VectorMatching

Abstract base class for all analyzer modules. Subclasses must implement the inputs and run methods.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
vectors_one_col	Column	Required
vectors_two_col	Column	Required
output_col	Column	Required
max_dpt	float | None	None

histogram_builder.py

HistogramBuilder

class analyzer.modules.common.histogram_builder.HistogramBuilder

Abstract base class for pure result modules. Subclasses must implement the outputs and run methods.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
product_name	str	Required
columns	list	Required
axes	list	Required
storage	str	'weight'
mask_col	Column | None	None

SimpleHistogram

class analyzer.modules.common.histogram_builder.SimpleHistogram

Abstract base class for all analyzer modules. Subclasses must implement the inputs and run methods.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
hist_name	str	Required
input_cols	list	Required
axes	list	Required
replace_none	float | None	None
mask_cols	list[Column] | None	None

hlt_selection.py

ComplexHLT

class analyzer.modules.common.hlt_selection.ComplexHLT

Analyzer module applying complex HLT-based selections with dataset-dependent trigger logic.

This module evaluates High-Level Trigger (HLT) decisions using a configurable set of trigger and veto definitions. The configuration is selected dynamically based on the dataset name using regular-expression pattern matching.

For a matched configuration:

• All configured trigger paths are OR-combined to form the pass condition.

• All configured veto paths are OR-combined to form the veto condition.

• The final selection is defined as:

  pass_trigger AND (NOT veto_trigger)
  

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
trigger_config	list	Required
selection_name	str	'PassHLT'

SaveHLT

class analyzer.modules.common.hlt_selection.SaveHLT

Save HLT triggers to the output columns.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
triggers	list	Required
save_name	str	'SavedHLT'

SimpleHLT

class analyzer.modules.common.hlt_selection.SimpleHLT

Select events based on HLT triggers.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
triggers	list	Required
selection_name	str	'PassHLT'

jets.py

FilterNear

class analyzer.modules.common.jets.FilterNear

Filter objects based on proximity to another collection.

This analyzer removes entries from a target collection that are within a specified angular distance ($Delta R$) of objects in a second collection. Objects in the target collection are kept only if no nearby object is found within the given distance threshold. Proximity is determined using the nearest method on the target collection.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
target_col	Column	Required
near_col	Column	Required
output_col	Column	Required
max_dr	float	Required

HT

class analyzer.modules.common.jets.HT

Compute the scalar sum of jet transverse momenta (H_T).

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
input_col	Column	Required
output_col	Column	Factory

JetComboHistograms

class analyzer.modules.common.jets.JetComboHistograms

Build composite objects from specified combinations of jets (by index) and produces histograms of their invariant mass and transverse momentum. Each combination is treated independently, and histograms are filled only for events where all required jets are present.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
prefix	str	Required
input_col	Column	Required
jet_combos	list	Required
mass_axis	RegularAxis	Factory

JetEtaPhiVeto

class analyzer.modules.common.jets.JetEtaPhiVeto

Event-level veto based on jet ($eta$, $phi$) regions.

This analyzer defines an event-level selection that vetoes events containing jets within a specified rectangular region in (η, φ) space. The veto can optionally be restricted to a specific run range.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
input_col	Column	Required
phi_range	tuple	Required
eta_range	tuple	Required
run_range	tuple[float, float] | None	None
selection_name	str	'jet_eta_phi_veto'

JetFilter

class analyzer.modules.common.jets.JetFilter

This analyzer filters an input jet collection according to transverse momentum and pseudorapidity requirements, with optional jet ID and pileup ID selections. The resulting filtered jet collection is written to a new output column.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
input_col	Column	Required
output_col	Column	Required
min_pt	float	30.0
max_abs_eta	float	2.4
include_pu_id	bool	False
include_jet_id	bool	False

JetResolutionCorrections

class analyzer.modules.common.jets.JetResolutionCorrections

This analyzer adjusts jet transverse momentum (pT) and mass to account for detector resolution effects in simulated (MC) events. Smearing is applied based on the per-jet resolution, matching to generated jets, and optional systematic variations.

Configuration Variables

Name	Type	Default
input_col	Column	Required
genjet_col	Column	Required
output_col	Column	Required
jet_type	str	'AK4'
use_regrouped	bool	True
should_run	MetadataExpr	Factory

JetScaleCorrections

class analyzer.modules.common.jets.JetScaleCorrections

This analyzer adjusts jet transverse momentum (pT) and mass according to jet energy corrections (JEC) derived from calibration campaigns. Both nominal (“central”) and systematic variations (up/down JES) are supported. Corrections are applied per jet based on its kinematics and dataset metadata.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
input_col	Column	Required
output_col	Column	Required
jet_type	str	'AK4'
use_regrouped	bool	True

PileupJetIdSF

class analyzer.modules.common.jets.PileupJetIdSF

Compute pileup jet ID scale factors for Monte Carlo events.

This analyzer calculates per-event weights corresponding to the pileup jet ID efficiency correction. Only jets with pT < 50 GeV and matched to a generator-level jet are considered.

Configuration Variables

Name	Type	Default
input_col	Column	Required
working_point	str	Required
weight_name	str	'puid_sf'
should_run	MetadataExpr	Factory

TopVecHistograms

class analyzer.modules.common.jets.TopVecHistograms

Produce kinematic histograms for the leading objects in a collection.

This analyzer creates histograms of $p_T$,$eta$, and $phi$ for the first N objects in a vector-like collection (e.g. jets), where N is configurable. Histograms are produced only for events where the corresponding object exists.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
prefix	str	Required
input_col	Column	Required
max_idx	int	Required

VetoMap

class analyzer.modules.common.jets.VetoMap

Event-level veto selection based on detector veto maps. Events are excluded if any jet passing a loose selection lies withing a vetoed $eta$-$phi$ region.

Configuration Variables

Name	Type	Default
input_col	Column	Required
selection_name	str	'jet_veto_map'
should_run	MetadataExpr	Factory
veto_type	str	'jetvetomap'

VetoMapFilter

class analyzer.modules.common.jets.VetoMapFilter

Apply a detector veto map to an input jet collection in order to remove jets falling into problematic detector regions. The veto map is evaluated as a function of jet ($eta$, $phi$) coordinates.

Configuration Variables

Name	Type	Default
input_col	Column	Required
output_col	Column	Required
should_run	MetadataExpr	Factory
veto_type	str	'jetvetomap'

load_columns.py

LoadColumns

class analyzer.modules.common.load_columns.LoadColumns

Abstract base class for event source modules. Subclasses must implement the outputs and run methods.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None

muons.py

MuonMaker

class analyzer.modules.common.muons.MuonMaker

Select muons based on kinematics, ID, and isolation criteria.

This analyzer filters muons in an event according to minimum transverse momentum, maximum pseudorapidity, a chosen ID working point, and optional isolation requirements.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
input_col	Column	Required
output_col	Column	Required
id_working_point	IdWps	Required
min_pt	float	10
max_abs_eta	float	2.4
max_mini_iso	float	0.1
iso_working_point	analyzer.modules.common.muons.IsoWps | None	None

save_arrays.py

SaveCols

class analyzer.modules.common.save_arrays.SaveCols

Abstract base class for all analyzer modules. Subclasses must implement the inputs and run methods.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
save_name	str	Required
to_save	dict	Required
remap_hlt_prefix	bool	True

selection.py

NObjFilter

class analyzer.modules.common.selection.NObjFilter

Select events based on the number of objects in a collection.

This analyzer filters events according to the number of objects

in a given column, requiring the count to be within specified limits.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
selection_name	str	Required
input_col	Column	Required
min_count	int | None	None
max_count	int | None	None

SelectOnColumns

class analyzer.modules.common.selection.SelectOnColumns

Apply a selection based on one or more boolean selection columns and optionally save the cutflow.

This analyzer performs an AND of the specified selection columns (or all default selections if none are provided) and filters the events accordingly. Optionally, it stores a cutflow summary for monitoring.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
sel_name	str	Required
selection_names	list[str] | None	None
save_cutflow	bool	True

skimming.py

SaveEvents

class analyzer.modules.common.skimming.SaveEvents

Analyzer module that serializes and persists event-level data to ROOT files.

SaveEvents writes the full event record from the current analysis columns to a ROOT file using uproot. Files are written locally first and then copied to a target destination defined by a configurable output path template.

Each unique input (as defined by getKeyNoParams) is written at most once per process execution.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
prefix	str	Required
output_format	str	'{dataset_name}__{sample_name}__{file_id}__{chunk.event_start}_{chunk.event_stop}.root'

Singlestop Modules

bnn_trig.py

MSDCleanerCategory

class analyzer.modules.singlestop.bnn_trig.MSDCleanerCategory

Abstract base class for all analyzer modules. Subclasses must implement the inputs and run methods.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None

MSDCleanerSelection

class analyzer.modules.singlestop.bnn_trig.MSDCleanerSelection

Abstract base class for all analyzer modules. Subclasses must implement the inputs and run methods.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
selection_name	str	'PassMSDCleaner'

TriggerBNN

class analyzer.modules.singlestop.bnn_trig.TriggerBNN

Compute trigger efficiency weights using a BNN ensemble.

This analyzer evaluates a Bayesian Neural Network on HT and leading fat jet pt to produce a trigger weight for MC samples.

Configuration Variables

Name	Type	Default
base_path	str	Required
net_pattern	str	Required
weight_name	str	'trigger_eff'
should_run	MetadataExpr	Factory

TriggerBNNCorrection

class analyzer.modules.singlestop.bnn_trig.TriggerBNNCorrection

Compute trigger efficiency weights using correctionlib for BNN.

Configuration Variables

Name	Type	Default
base_path	str	Required
correction_pattern	str	Required
correction_name	str	'trigger_eff'
weight_name	str	'trigger_eff'
should_run	MetadataExpr	Factory

nn_reco.py

NNMassPlots

class analyzer.modules.singlestop.nn_reco.NNMassPlots

Create histograms for reconstructed mass variables.

Generates 1D and 2D histograms for the reconstructed top squark and chargino masses, as well as their ratio.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
m3_input	Column	Required
m4_input	Column	Required
prefix	str	Required

NNMassReco

class analyzer.modules.singlestop.nn_reco.NNMassReco

Reconstruct top quark and neutralino masses using a Neural Network.

This module uses a trained PyTorch model to resolve jet combinatorics and reconstruct the mass of the top quark ($m_{tilde{t}}$) and neutralino ($m_{chi}$).

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
input_col	Column	Required
m3_output	Column	Required
m4_output	Column	Required
model_path	str	Required
scaler_path	str	Required

selections.py

VecDRSelection

class analyzer.modules.singlestop.selections.VecDRSelection

Abstract base class for all analyzer modules. Subclasses must implement the inputs and run methods.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
input_col	Column	Required
selection_name	str	Required
min_dr	float | None	None
max_dr	float | None	None
idx_1	int	0
idx_2	int	1

VecPt

class analyzer.modules.singlestop.selections.VecPt

Abstract base class for all analyzer modules. Subclasses must implement the inputs and run methods.

Configuration Variables

Name	Type	Default
should_run	MetadataExpr | None	None
selection_name	str	Required
input_col	Column	Required
idx	int	0
min_pt	float | None	None
max_pt	float | None	None