firecrown.likelihood.weak_lensing

Weak lensing source and systematics.

Attributes

MULTIPLICATIVE_SHEAR_BIAS_DEFAULT_BIAS
LINEAR_ALIGNMENT_DEFAULT_IA_BIAS
LINEAR_ALIGNMENT_DEFAULT_ALPHAZ
LINEAR_ALIGNMENT_DEFAULT_ALPHAG
LINEAR_ALIGNMENT_DEFAULT_Z_PIV
TATT_ALIGNMENT_DEFAULT_IA_A_1
TATT_ALIGNMENT_DEFAULT_IA_A_2
TATT_ALIGNMENT_DEFAULT_IA_A_D
HM_ALIGNMENT_DEFAULT_IA_A_1H
HM_ALIGNMENT_DEFAULT_IA_A_2H
WeakLensingSystematicFactory

Classes

WeakLensingArgs	Class for weak lensing tracer builder argument.
WeakLensingSystematic	Abstract base class for all weak lensing systematics.
PhotoZShiftandStretch	Photo-z shift systematic.
PhotoZShift	Photo-z shift systematic.
SelectField	Systematic to select 3D field.
MultiplicativeShearBias	Multiplicative shear bias systematic.
LinearAlignmentSystematic	Linear alignment systematic.
TattAlignmentSystematic	TATT alignment systematic.
HMAlignmentSystematic	Halo model intrinsic alignment systematic.
WeakLensing	Source class for weak lensing.
MultiplicativeShearBiasFactory	Factory class for MultiplicativeShearBias objects.
LinearAlignmentSystematicFactory	Factory class for LinearAlignmentSystematic objects.
TattAlignmentSystematicFactory	Factory class for TattAlignmentSystematic objects.
WeakLensingFactory	Factory class for WeakLensing objects.

Module Contents

class firecrown.likelihood.weak_lensing.WeakLensingArgs

Bases: firecrown.likelihood.source.SourceGalaxyArgs

Class for weak lensing tracer builder argument.

ia_bias: None | tuple[numpy.typing.NDArray[numpy.float64], numpy.typing.NDArray[numpy.float64]] = None

has_pt: bool = False

has_hm: bool = False

ia_pt_c_1: None | tuple[numpy.typing.NDArray[numpy.float64], numpy.typing.NDArray[numpy.float64]] = None

ia_pt_c_d: None | tuple[numpy.typing.NDArray[numpy.float64], numpy.typing.NDArray[numpy.float64]] = None

ia_pt_c_2: None | tuple[numpy.typing.NDArray[numpy.float64], numpy.typing.NDArray[numpy.float64]] = None

ia_a_1h: None | numpy.typing.NDArray[numpy.float64] = None

ia_a_2h: None | numpy.typing.NDArray[numpy.float64] = None

class firecrown.likelihood.weak_lensing.WeakLensingSystematic(parameter_prefix=None)

Bases: firecrown.likelihood.source.SourceGalaxySystematic[WeakLensingArgs]

Abstract base class for all weak lensing systematics.

Parameters:

parameter_prefix (None | str)

abstractmethod apply(tools, tracer_arg)

Apply method to include systematics in the tracer_arg.

Parameters:

• tools (firecrown.modeling_tools.ModelingTools)

• tracer_arg (WeakLensingArgs)

Return type:

WeakLensingArgs

class firecrown.likelihood.weak_lensing.PhotoZShiftandStretch(sacc_tracer, active=True)

Bases: firecrown.likelihood.source.SourceGalaxyPhotoZShiftandStretch[WeakLensingArgs]

Photo-z shift systematic.

Parameters:

• sacc_tracer (str)

• active (bool)

class firecrown.likelihood.weak_lensing.PhotoZShift(sacc_tracer, active=True)

Bases: firecrown.likelihood.source.SourceGalaxyPhotoZShift[WeakLensingArgs]

Photo-z shift systematic.

Parameters:

• sacc_tracer (str)

• active (bool)

class firecrown.likelihood.weak_lensing.SelectField(field='delta_matter')

Bases: firecrown.likelihood.source.SourceGalaxySelectField[WeakLensingArgs]

Systematic to select 3D field.

Parameters:

field (str)

firecrown.likelihood.weak_lensing.MULTIPLICATIVE_SHEAR_BIAS_DEFAULT_BIAS = 1.0

class firecrown.likelihood.weak_lensing.MultiplicativeShearBias(sacc_tracer)

Bases: WeakLensingSystematic

Multiplicative shear bias systematic.

This systematic adjusts the scale_ of a source by (1 + m).

The following parameters are special Updatable parameters, which means that they can be updated by the sampler, sacc_tracer is going to be used as a prefix for the parameters:

Variables:

mult_bias – the multiplicative shear bias parameter.

Parameters:

sacc_tracer (str)

mult_bias

apply(tools, tracer_arg)

Apply multiplicative shear bias to a source.

The scale_ of the source is multiplied by (1 + m).

Parameters:

• tools (firecrown.modeling_tools.ModelingTools) – A ModelingTools object.

• tracer_arg (WeakLensingArgs) – The WeakLensingArgs to which apply the shear bias.

Returns:

A new WeakLensingArgs object with the shear bias applied.

Return type:

WeakLensingArgs

firecrown.likelihood.weak_lensing.LINEAR_ALIGNMENT_DEFAULT_IA_BIAS = 0.5

firecrown.likelihood.weak_lensing.LINEAR_ALIGNMENT_DEFAULT_ALPHAZ = 0.0

firecrown.likelihood.weak_lensing.LINEAR_ALIGNMENT_DEFAULT_ALPHAG = 1.0

firecrown.likelihood.weak_lensing.LINEAR_ALIGNMENT_DEFAULT_Z_PIV = 0.5

class firecrown.likelihood.weak_lensing.LinearAlignmentSystematic(sacc_tracer=None, alphag=1.0)

Bases: WeakLensingSystematic

Linear alignment systematic.

This systematic adds a linear intrinsic alignment model systematic which varies with redshift and the growth function.

The following parameters are special Updatable parameters, which means that they can be updated by the sampler, sacc_tracer is going to be used as a prefix for the parameters:

Variables:

• ia_bias – the intrinsic alignment bias parameter.

• alphaz – the redshift dependence of the intrinsic alignment bias.

• alphag – the growth function dependence of the intrinsic alignment bias.

• z_piv – the pivot redshift for the intrinsic alignment bias.

Parameters:

• sacc_tracer (None | str)

• alphag (None | float)

ia_bias

alphaz

alphag

z_piv

apply(tools, tracer_arg)

Return a new linear alignment systematic.

This choice is based on the given tracer_arg, in the context of the given cosmology.

Parameters:

• tools (firecrown.modeling_tools.ModelingTools)

• tracer_arg (WeakLensingArgs)

Return type:

WeakLensingArgs

firecrown.likelihood.weak_lensing.TATT_ALIGNMENT_DEFAULT_IA_A_1 = 1.0

firecrown.likelihood.weak_lensing.TATT_ALIGNMENT_DEFAULT_IA_A_2 = 0.5

firecrown.likelihood.weak_lensing.TATT_ALIGNMENT_DEFAULT_IA_A_D = 0.5

class firecrown.likelihood.weak_lensing.TattAlignmentSystematic(sacc_tracer=None)

Bases: WeakLensingSystematic

TATT alignment systematic.

This systematic adds a TATT (nonlinear) intrinsic alignment model systematic.

The following parameters are special Updatable parameters, which means that they can be updated by the sampler, sacc_tracer is going to be used as a prefix for the parameters:

Variables:

• ia_a_1 – the amplitude of the linear alignment model.

• ia_a_2 – the amplitude of the quadratic alignment model.

• ia_a_d – the amplitude of the density-dependent alignment model.

Parameters:

sacc_tracer (None | str)

ia_a_1

ia_a_2

ia_a_d

apply(tools, tracer_arg)

Return a new linear alignment systematic.

This choice is based on the given tracer_arg, in the context of the given cosmology.

Parameters:

• tools (firecrown.modeling_tools.ModelingTools)

• tracer_arg (WeakLensingArgs)

Return type:

WeakLensingArgs

firecrown.likelihood.weak_lensing.HM_ALIGNMENT_DEFAULT_IA_A_1H = 0.0001

firecrown.likelihood.weak_lensing.HM_ALIGNMENT_DEFAULT_IA_A_2H = 1.0

class firecrown.likelihood.weak_lensing.HMAlignmentSystematic(_=None)

Bases: WeakLensingSystematic

Halo model intrinsic alignment systematic.

This systematic adds a halo model based intrinsic alignment systematic which, at the moment, is fixed within the redshift bin.

The following parameters are special Updatable parameters, which means that they can be updated by the sampler, sacc_tracer is going to be used as a prefix for the parameters:

Variables:

• ia_a_1h – the 1-halo intrinsic alignment bias parameter (satellite galaxies).

• ia_a_2h – the 2-halo intrinsic alignment bias parameter (central galaxies).

Parameters:

_ (None | str)

ia_a_1h

ia_a_2h

apply(tools, tracer_arg)

Return a new halo-model alignment systematic.

Parameters:

• tools (firecrown.modeling_tools.ModelingTools) – A ModelingTools object.

• tracer_arg (WeakLensingArgs) – The WeakLensingArgs to which apply the systematic.

Returns:

A new WeakLensingArgs object with the systematic applied.

Return type:

WeakLensingArgs

class firecrown.likelihood.weak_lensing.WeakLensing(*, sacc_tracer, scale=1.0, systematics=None)

Bases: firecrown.likelihood.source.SourceGalaxy[WeakLensingArgs]

Source class for weak lensing.

Parameters:

• sacc_tracer (str)

• scale (float)

• systematics (None | Sequence[firecrown.likelihood.source.SourceGalaxySystematic[WeakLensingArgs]])

sacc_tracer

scale = 1.0

current_tracer_args: None | WeakLensingArgs = None

tracer_args: WeakLensingArgs

classmethod create_ready(inferred_zdist, systematics=None)

Create a WeakLensing object with the given tracer name and scale.

Parameters:

• inferred_zdist (firecrown.metadata_types.InferredGalaxyZDist)

• systematics (None | list[firecrown.likelihood.source.SourceGalaxySystematic[WeakLensingArgs]])

Return type:

WeakLensing

create_tracers(tools)

Render a source by applying systematics.

Parameters:

tools (firecrown.modeling_tools.ModelingTools)

get_scale()

Returns the scales for this Source.

class firecrown.likelihood.weak_lensing.MultiplicativeShearBiasFactory(/, **data)

Bases: pydantic.BaseModel

Factory class for MultiplicativeShearBias objects.

Parameters:

data (Any)

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

type: Annotated[Literal['MultiplicativeShearBiasFactory'], Field(description='The type of the systematic.')] = 'MultiplicativeShearBiasFactory'

create(bin_name)

Create a MultiplicativeShearBias object.

Parameters:

• inferred_zdist – The inferred galaxy redshift distribution for the created MultiplicativeShearBias object.

• bin_name (str)

Returns:

The created MultiplicativeShearBias object.

Return type:

MultiplicativeShearBias

create_global()

Create a MultiplicativeShearBias object.

Returns:

The created MultiplicativeShearBias object.

Return type:

MultiplicativeShearBias

class firecrown.likelihood.weak_lensing.LinearAlignmentSystematicFactory(/, **data)

Bases: pydantic.BaseModel

Factory class for LinearAlignmentSystematic objects.

Parameters:

data (Any)

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

type: Annotated[Literal['LinearAlignmentSystematicFactory'], Field(description='The type of the systematic.')] = 'LinearAlignmentSystematicFactory'

alphag: None | float = 1.0

create(bin_name)

Create a LinearAlignmentSystematic object.

Parameters:

• inferred_zdist – The inferred galaxy redshift distribution for the created LinearAlignmentSystematic object.

• bin_name (str)

Returns:

The created LinearAlignmentSystematic object.

Return type:

LinearAlignmentSystematic

create_global()

Create a LinearAlignmentSystematic object.

Returns:

The created LinearAlignmentSystematic object.

Return type:

LinearAlignmentSystematic

class firecrown.likelihood.weak_lensing.TattAlignmentSystematicFactory(/, **data)

Bases: pydantic.BaseModel

Factory class for TattAlignmentSystematic objects.

Parameters:

data (Any)

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

type: Annotated[Literal['TattAlignmentSystematicFactory'], Field(description='The type of the systematic.')] = 'TattAlignmentSystematicFactory'

create(bin_name)

Create a TattAlignmentSystematic object.

Parameters:

• inferred_zdist – The inferred galaxy redshift distribution for the created TattAlignmentSystematic object.

• bin_name (str)

Returns:

The created TattAlignmentSystematic object.

Return type:

TattAlignmentSystematic

create_global()

Create a TattAlignmentSystematic object.

Returns:

The created TattAlignmentSystematic object.

Return type:

TattAlignmentSystematic

firecrown.likelihood.weak_lensing.WeakLensingSystematicFactory

class firecrown.likelihood.weak_lensing.WeakLensingFactory(/, **data)

Bases: pydantic.BaseModel

Factory class for WeakLensing objects.

Parameters:

data (Any)

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

per_bin_systematics: Sequence[WeakLensingSystematicFactory]

global_systematics: Sequence[WeakLensingSystematicFactory]

model_post_init(_, /)

Initialize the WeakLensingFactory object.

Return type:

None

create(inferred_zdist)

Create a WeakLensing object with the given tracer name and scale.

Parameters:

inferred_zdist (firecrown.metadata_types.InferredGalaxyZDist)

Return type:

WeakLensing

create_from_metadata_only(sacc_tracer)

Create an WeakLensing object with the given tracer name and scale.

Parameters:

sacc_tracer (str)

Return type:

WeakLensing