scale

Column production methods related to the renormalization and factorization scales.

Classes:

ScaleWeightOutput(value)	Flag to denote which type of weights to output in the murmuf_weights producer.
murmuf_weights(*args, **kwargs)
murmuf_weights_raw(*args, **kwargs)
murmuf_envelope_weights(*args, **kwargs)

class ScaleWeightOutput(value)

Bases: StrEnum

Flag to denote which type of weights to output in the murmuf_weights producer. Options:

• single: Produces {mur,muf}_weight{,_up,_down}.

• correlated: Produces murmuf_weight{,_up,_down}.

• single_correlated: Produces {mur,muf,murmuf}_weight{,_up,_down}.

• raw: Produces all combinations mur_{nom,up,down}_muf_{nom,up,down}.

Attributes:

single
correlated
single_correlated
raw

single = 'single'

correlated = 'correlated'

single_correlated = 'single_correlated'

raw = 'raw'

class murmuf_weights(*args, **kwargs)

Bases: Producer

Attributes:

cache_instances
mc_only
scale_weight_output
uses

Methods:

call_func(events, **kwargs)	Producer that reads out mur and muf uncertainties on an event-by-event basis.
init_func(**kwargs)	Default init function.
skip_func(**kwargs)	Default skip function.
update_cls_dict(cls_name, cls_dict, get_attr)

cache_instances = True

call_func(events, **kwargs)

Producer that reads out mur and muf uncertainties on an event-by-event basis. This producer assumes that the nominal entry is always the 5th LHEScaleWeight entry and that the nominal weight is already included in the LHEWeight. Can only be called with MC datasets.

Return type:

Array

Resources:

• https://cms-xpog.docs.cern.ch/autoDoc/NanoAODv15/2024/doc_TTH-Hto2G_Par-M-125_TuneCP5_13p6TeV_amcatnloFXFX-pythia8_RunIII2024Summer24NanoAODv15-150X_mcRun3_2024_realistic_v2-v2.html#LHEScaleWeight # noqa

init_func(**kwargs)

Default init function.

Return type:

None

mc_only: bool = True

scale_weight_output = 'single_correlated'

skip_func(**kwargs) → bool

Default skip function.

Return type:

bool

static update_cls_dict(cls_name, cls_dict, get_attr)

uses = {'LHEScaleWeight'}

class murmuf_weights_raw(*args, **kwargs)

Bases: murmuf_weights

Attributes:

cache_instances
scale_weight_output

Methods:

skip_func(**kwargs)

Default skip function.

cache_instances = True

scale_weight_output = 'raw'

skip_func(**kwargs) → bool

Default skip function.

Return type:

bool

class murmuf_envelope_weights(*args, **kwargs)

Bases: Producer

Attributes:

cache_instances
envelope_columns
mc_only
produces
uses

Methods:

call_func(events, **kwargs)	Producer that determines the envelope of the mur/muf up and down variations on an event-by-event basis.
skip_func(**kwargs)	Default skip function.
update_cls_dict(cls_name, cls_dict, get_attr)

cache_instances = True

call_func(events, **kwargs)

Producer that determines the envelope of the mur/muf up and down variations on an event-by-event basis. See murmuf_weights for details on the assumptions made about the LHEScaleWeight vector.

Return type:

Array

envelope_columns = ['mur_down_muf_down', 'mur_down_muf_nom', 'mur_nom_muf_down', 'mur_nom_muf_nom', 'mur_nom_muf_up', 'mur_up_muf_nom', 'mur_up_muf_up']

mc_only: bool = True

produces = {'murmuf_envelope_weight{,_up,_down}'}

skip_func(**kwargs) → bool

Default skip function.

Return type:

bool

static update_cls_dict(cls_name, cls_dict, get_attr)

uses = {<class 'columnflow.production.cms.scale.murmuf_weights_raw'>}