firecrown.likelihood.weak_lensing

Weak lensing source and systematics.

This subpackage provides weak lensing source classes and systematics for use in likelihood calculations.

Classes

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

Package Contents

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.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

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

Bases: WeakLensingSystematic

Mass-dependent linear alignment systematic.

Adds a linear intrinsic alignment model systematic the amplitude of which depends on the assumed model mass scaling, red fraction, and average halo mass of the tracer. Blue galaxies are assumed to have zero intrinsic alignment amplitude.

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_amplitude – the intrinsic alignment amplitude at the pivot halo mass.

• ia_mass_scaling – the power-law index of the model’s mass scaling.

• red_fraction – the red galaxy fraction of the tracer sample.

• log10_average_halo_mass – the 10-base logarithm of the average halo mass of the tracer sample (mass should be given in units of solar mass / h).

Parameters:

sacc_tracer (None | str)

The following parameter is an InternalParameter that will not be provided by the sampler, instead the value given will be used throughout all calculations:

Variables:

pivot_log10_halo_mass – the log10-base of the pivot halo mass of the model (default=13.5, pivot mass in M_sun/h).

Parameters:

sacc_tracer (None | str)

ia_amplitude

ia_mass_scaling

red_fraction

log10_average_halo_mass

pivot_log10_halo_mass

apply(tools, tracer_arg)

Return a mass-dependent 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

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.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

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.PhotoZShift(sacc_tracer, active=True)

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

Photo-z shift systematic.

Parameters:

• sacc_tracer (str)

• active (bool)

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

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

Photo-z shift systematic.

Parameters:

• sacc_tracer (str)

• active (bool)

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

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

Systematic to select 3D field.

Parameters:

field (str)

class firecrown.likelihood.weak_lensing.TattAlignmentSystematic(sacc_tracer=None, include_z_dependence=False)

Bases: WeakLensingSystematic

TATT alignment systematic.

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

The amplitude of each contribution to the TATT model (i.e. linear, density-dependent, or quadratic terms) can be expressed as a function in redshift, parameterized by the relationship: $A_i times frac{1 + z}{1 + z_{piv,i}}^{alpha_i}$

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_zpiv_1 – the pivot redshift of the linear alignment model.

• ia_alphaz_1 – the redshift dependence of the linear alignment model.

• ia_a_2 – the amplitude of the quadratic alignment model.

• ia_zpiv_2 – the pivot redshift of the quadratic alignment model.

• ia_alphaz_2 – the redshift dependence of the quadratic alignment model.

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

• ia_zpiv_d – the pivot redshift of the density-dependent alignment model.

• ia_alphaz_d – the redshift dependence of the density-dependent alignment model.

Parameters:

• sacc_tracer (None | str)

• include_z_dependence (bool)

ia_a_1

ia_zpiv_1

ia_alphaz_1

ia_a_2

ia_zpiv_2

ia_alphaz_2

ia_a_d

ia_zpiv_d

ia_alphaz_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

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'

include_z_dependence: bool = False

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

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

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

Source class for weak lensing.

Parameters:

• sacc_tracer (str)

• scale (float)

• systematics (None | collections.abc.Sequence[firecrown.likelihood._base.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._base.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.WeakLensingArgs

Bases: firecrown.likelihood._base.SourceGalaxyArgs

Class for weak lensing tracer builder argument.

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

ia_amplitude: None | numpy.float64 = None

ia_mass_scaling: None | numpy.float64 = None

red_fraction: None | numpy.float64 = None

log10_average_halo_mass: None | 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.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].

type_source: firecrown.metadata_types.TypeSource

per_bin_systematics: collections.abc.Sequence[WeakLensingSystematicFactory] = None

global_systematics: collections.abc.Sequence[WeakLensingSystematicFactory] = None

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

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

Bases: firecrown.likelihood._base.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.PhotoZShiftFactory(/, **data)

Bases: pydantic.BaseModel

Factory class for PhotoZShift objects.

Parameters:

data (Any)

model_config

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

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

create(bin_name)

Create a PhotoZShift object with the given tracer name.

Parameters:

bin_name (str)

Return type:

PhotoZShift

create_global()

Create a PhotoZShift object with the given tracer name.

Return type:

PhotoZShift

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

Bases: pydantic.BaseModel

Factory class for PhotoZShiftandStretch objects.

Parameters:

data (Any)

model_config

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

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

create(bin_name)

Create a PhotoZShiftandStretch object with the given tracer name.

Parameters:

bin_name (str)

Return type:

PhotoZShiftandStretch

create_global()

Create a PhotoZShiftandStretch object with the given tracer name.

Return type:

PhotoZShiftandStretch