analyzer.modules.singlestop.bnn_trig¶

Classes¶

`BNNEnsemble`
`TriggerBNN`	Compute trigger efficiency weights using a BNN ensemble.
`TriggerBNNCorrection`	Compute trigger efficiency weights using correctionlib for BNN.
`MSDCleanerCategory`	Abstract base class for all analyzer modules.
`MSDCleanerSelection`	Abstract base class for all analyzer modules.

Module Contents¶

class analyzer.modules.singlestop.bnn_trig.BNNEnsemble(num_inputs, mean, sigma, hidden_width, data)[source]¶

mean[source]¶

sigma[source]¶

output_bias[source]¶

output_weights[source]¶

hidden_biases[source]¶

hidden_weights[source]¶

__call__(data)[source]¶

static fromFile(path)[source]¶

class analyzer.modules.singlestop.bnn_trig.TriggerBNN[source]¶

Bases: analyzer.core.analysis_modules.AnalyzerModule

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.

Parameters¶

base_pathstr: Base directory where BNN JSON files are stored.
net_patternstr: Pattern for the network filename, formatted with the era name.
weight_namestr, optional: Name of the output weight column, by default “trigger_eff”.
should_runMetadataExpr, optional: Condition to determine if the module should run. By default runs on MC samples.

base_path: str[source]¶

net_pattern: str[source]¶

weight_name: str = 'trigger_eff'[source]¶

should_run: analyzer.core.analysis_modules.MetadataExpr[source]¶

inputs(metadata)[source]¶

outputs(metadata)[source]¶

neededResources(metadata)[source]¶

getParameterSpec(metadata)[source]¶

getBNN(metadata)[source]¶

run(columns, params)[source]¶

class analyzer.modules.singlestop.bnn_trig.TriggerBNNCorrection[source]¶

Bases: analyzer.core.analysis_modules.AnalyzerModule

Compute trigger efficiency weights using correctionlib for BNN.

Parameters¶

base_pathstr: Base directory where correction files are stored.
correction_patternstr: Pattern for the correction filename, formatted with the era name (e.g. bnn_correction_{era}.json.gz).
correction_namestr, optional: Name of the correction in the file, by default “BNN_Trigger_Efficiency”.
weight_namestr, optional: Name of the output weight column, by default “trigger_eff”.
should_runMetadataExpr, optional: Condition to determine if the module should run. By default runs on MC samples.

base_path: str[source]¶

correction_pattern: str[source]¶

correction_name: str = 'trigger_eff'[source]¶

weight_name: str = 'trigger_eff'[source]¶

should_run: analyzer.core.analysis_modules.MetadataExpr[source]¶

inputs(metadata)[source]¶

outputs(metadata)[source]¶

neededResources(metadata)[source]¶

getParameterSpec(metadata)[source]¶

getCorrection(metadata)[source]¶

run(columns, params)[source]¶

class analyzer.modules.singlestop.bnn_trig.MSDCleanerCategory[source]¶

Bases: analyzer.core.analysis_modules.AnalyzerModule

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

inputs(metadata)[source]¶

outputs(metadata)[source]¶

run(columns, params)[source]¶

class analyzer.modules.singlestop.bnn_trig.MSDCleanerSelection[source]¶

Bases: analyzer.core.analysis_modules.AnalyzerModule

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

selection_name: str = 'PassMSDCleaner'[source]¶

inputs(metadata)[source]¶

outputs(metadata)[source]¶

run(columns, params)[source]¶