def with_lens_light(
settings_autofit: slam_util.SettingsAutoFit,
analysis: Union[al.AnalysisImaging, al.AnalysisInterferometer],
setup_hyper: al.SetupHyper,
source_results: af.ResultsCollection,
light_results: af.ResultsCollection,
lens_bulge: af.Model(al.lp.LightProfile) = af.Model(al.lp.EllSersic),
lens_disk: af.Model(al.lp.LightProfile) = None,
lens_envelope: af.Model(al.lp.LightProfile) = None,
dark: af.Model(al.mp.MassProfile) = af.Model(al.mp.EllNFWMCRLudlow),
smbh: af.Model(al.mp.MassProfile) = None,
einstein_mass_range: Optional[Tuple[float, float]] = (0.01, 5.0),
end_with_hyper_extension: bool = False,
) -> af.ResultsCollection:
"""
The SLaM MASS LIGHT DARK PIPELINE for fitting imaging data with a lens light component.
Parameters
----------
analysis
The analysis class which includes the `log_likelihood_function` and can be customized for the SLaM model-fit.
setup_hyper
The setup of the hyper analysis if used (e.g. hyper-galaxy noise scaling).
source_results
The results of the SLaM SOURCE PARAMETRIC PIPELINE or SOURCE INVERSION PIPELINE which ran before this pipeline.
light_results
The results of the SLaM LIGHT PARAMETRIC PIPELINE which ran before this pipeline.
mass
The `MassProfile` used to fit the lens galaxy mass in this pipeline.
smbh
The `MassProfile` used to fit the a super massive black hole in the lens galaxy.
lens_bulge
The `LightMassProfile` `Model` used to represent the light and stellar mass distribution of the lens galaxy's
bulge (set to None to omit a bulge).
lens_disk
The `LightMassProfile` `Model` used to represent the light and stellar mass distribution of the lens galaxy's
disk (set to None to omit a disk).
lens_envelope
The `LightMassProfile` `Model` used to represent the light and stellar mass distribution of the lens galaxy's
envelope (set to None to omit an envelope).
dark
The `MassProfile` `Model` used to represent the dark matter distribution of the lens galaxy's (set to None to
omit dark matter).
einstein_mass_range
The values a the estimate of the Einstein Mass in the LIGHT PIPELINE is multiplied by to set the lower and
upper limits of the profile's mass-to-light ratio.
end_with_hyper_extension
If `True` a hyper extension is performed at the end of the pipeline. If this feature is used, you must be
certain you have manually passed the new hyper images geneted in this search to the next pipelines.
"""
"""
__Model + Search + Analysis + Model-Fit (Search 1)__
In search 1 of the MASS LIGHT DARK PIPELINE we fit a lens model where:
- The lens galaxy light and stellar mass is modeled using light and mass profiles [Priors on light model parameters
initialized from LIGHT PIPELINE].
- The lens galaxy dark mass is modeled using a dark mass distribution [No prior initialization].
- The source galaxy's light is parametric or an inversion depending on the previous pipeline [Model and priors
initialized from SOURCE PIPELINE].
This search aims to accurately estimate the lens mass model, using the improved mass model priors and source model
of the SOURCE PIPELINE and LIGHT PIPELINE.
The `mass_to_light_ratio` prior of each light and stellar profile is set using the Einstein Mass estimate of the
SOURCE PIPELINE, specifically using values which are 1% and 500% this estimate.
The dark matter mass profile has the lens and source redshifts added to it, which are used to determine its mass
from the mass-to-concentration relation of Ludlow et al.
"""
lens_bulge = slam_util.pass_light_and_mass_profile_priors(
model=lens_bulge,
result_light_component=light_results.last.model.galaxies.lens.bulge,
result=light_results.last,
einstein_mass_range=einstein_mass_range,
)
lens_disk = slam_util.pass_light_and_mass_profile_priors(
model=lens_disk,
result_light_component=light_results.last.model.galaxies.lens.disk,
result=light_results.last,
einstein_mass_range=einstein_mass_range,
)
lens_envelope = slam_util.pass_light_and_mass_profile_priors(
model=lens_envelope,
result_light_component=light_results.last.model.galaxies.lens.envelope,
result=light_results.last,
einstein_mass_range=einstein_mass_range,
)
dark.mass_at_200 = af.LogUniformPrior(lower_limit=1e10, upper_limit=1e15)
dark.redshift_object = light_results.last.instance.galaxies.lens.redshift
dark.redshift_source = light_results.last.instance.galaxies.source.redshift
if smbh is not None:
smbh.centre = lens_bulge.centre
source = slam_util.source__from_result_model_if_parametric(
result=source_results.last, setup_hyper=setup_hyper
)
model = af.Collection(
galaxies=af.Collection(
lens=af.Model(
al.Galaxy,
redshift=light_results.last.instance.galaxies.lens.redshift,
bulge=lens_bulge,
disk=lens_disk,
envelope=lens_envelope,
dark=dark,
shear=source_results.last.model.galaxies.lens.shear,
smbh=smbh,
hyper_galaxy=setup_hyper.hyper_galaxy_lens_from_result(
result=light_results.last
),
),
source=source,
),
hyper_image_sky=setup_hyper.hyper_image_sky_from_result(
result=light_results.last, as_model=True
),
hyper_background_noise=setup_hyper.hyper_background_noise_from_result(
result=light_results.last
),
)
search = af.DynestyStatic(
path_prefix=settings_autofit.path_prefix,
name="mass_light_dark[1]_light[parametric]_mass[light_dark]_source",
unique_tag=settings_autofit.unique_tag,
number_of_cores=settings_autofit.number_of_cores,
session=settings_autofit.session,
nlive=100,
)
result_1 = search.fit(model=model, analysis=analysis, info=settings_autofit.info)
"""
__Hyper Extension__
The above search may be extended with a hyper-search, if the SetupHyper has one or more of the following inputs:
- The source is using an `Inversion`.
- One or more `HyperGalaxy`'s are included.
- The background sky is included via `hyper_image_sky` input.
- The background noise is included via the `hyper_background_noise`.
"""
if end_with_hyper_extension:
result_1 = extensions.hyper_fit(
setup_hyper=setup_hyper,
result=result_1,
analysis=analysis,
include_hyper_image_sky=True,
)
return af.ResultsCollection([result_1])
def detection_single_plane(
path_prefix: str,
analysis: Union[al.AnalysisImaging, al.AnalysisInterferometer],
setup_hyper: al.SetupHyper,
mass_results: af.ResultsCollection,
subhalo_mass: af.Model(al.mp.MassProfile) = af.Model(
al.mp.SphNFWMCRLudlow),
grid_dimension_arcsec: float = 3.0,
number_of_steps: Union[Tuple[int], int] = 5,
number_of_cores: int = 1,
unique_tag: Optional[str] = None,
session: Optional[bool] = None,
) -> af.ResultsCollection:
"""
The SLaM SUBHALO PIPELINE for fitting imaging data with or without a lens light component, where it is assumed
that the subhalo is at the same redshift as the lens galaxy.
Parameters
----------
path_prefix
The prefix of folders between the output path and the search folders.
analysis
The analysis class which includes the `log_likelihood_function` and can be customized for the SLaM model-fit.
setup_hyper
The setup of the hyper analysis if used (e.g. hyper-galaxy noise scaling).
mass_results
The results of the SLaM MASS PIPELINE which ran before this pipeline.
subhalo_mass
The `MassProfile` used to fit the subhalo in this pipeline.
grid_dimension_arcsec
the arc-second dimensions of the grid in the y and x directions. An input value of 3.0" means the grid in
all four directions extends to 3.0" giving it dimensions 6.0" x 6.0".
number_of_steps
The 2D dimensions of the grid (e.g. number_of_steps x number_of_steps) that the subhalo search is performed for.
number_of_cores
The number of cores used to perform the non-linear search grid search. If 1, each model-fit on the grid is
performed in serial, if > 1 fits are distributed in parallel using the Python multiprocessing module.
unique_tag
The unique tag for this model-fit, which will be given a unique entry in the sqlite database and also acts as
the folder after the path prefix and before the search name. This is typically the name of the dataset.
"""
"""
__Model + Search + Analysis + Model-Fit (Search 1)__
In search 1 of the SUBHALO PIPELINE we fit a lens model where:
- The lens galaxy mass is modeled using MASS PIPELINE's mass distribution [Priors initialized from MASS PIPELINE].
- The source galaxy's light is parametric or an inversion depending on the previous MASS PIPELINE [Model and
priors initialized from MASS PIPELINE].
This search aims to accurately estimate the lens mass model, using the improved mass model priors and source model
of the MASS PIPELINE. This model will be used to perform Bayesian model comparison with models that include a
subhalo, to determine if a subhalo is detected.
"""
source = slam_util.source__from_result_model_if_parametric(
result=mass_results.last, setup_hyper=setup_hyper)
model = af.Collection(
galaxies=af.Collection(lens=mass_results.last.model.galaxies.lens,
source=source),
hyper_image_sky=setup_hyper.hyper_image_sky_from_result(
result=mass_results.last, as_model=True),
hyper_background_noise=setup_hyper.hyper_background_noise_from_result(
result=mass_results.last),
)
search = af.DynestyStatic(
path_prefix=path_prefix,
name="subhalo[1]_mass[total_refine]",
unique_tag=unique_tag,
session=session,
nlive=100,
)
result_1 = search.fit(model=model, analysis=analysis)
"""
__Model + Search + Analysis + Model-Fit (Search 2)__
In search 2 of the SUBHALO PIPELINE we perform a [number_of_steps x number_of_steps] grid search of non-linear
searches where:
- The lens galaxy mass is modeled using MASS PIPELINE's mass distribution [Priors initialized from MASS PIPELINE].
- The source galaxy's light is parametric or an inversion depending on the previous MASS PIPELINE [Model and
priors initialized from MASS PIPELINE].
- The subhalo redshift is fixed to that of the lens galaxy.
- Each grid search varies the subhalo (y,x) coordinates and mass as free parameters.
- The priors on these (y,x) coordinates are UniformPriors, with limits corresponding to the grid-cells.
This search aims to detect a dark matter subhalo.
"""
subhalo = af.Model(al.Galaxy,
redshift=result_1.instance.galaxies.lens.redshift,
mass=subhalo_mass)
subhalo.mass.mass_at_200 = af.LogUniformPrior(lower_limit=1.0e6,
upper_limit=1.0e11)
subhalo.mass.centre_0 = af.UniformPrior(lower_limit=-grid_dimension_arcsec,
upper_limit=grid_dimension_arcsec)
subhalo.mass.centre_1 = af.UniformPrior(lower_limit=-grid_dimension_arcsec,
upper_limit=grid_dimension_arcsec)
subhalo.mass.redshift_object = result_1.instance.galaxies.lens.redshift
subhalo.mass.redshift_source = result_1.instance.galaxies.source.redshift
source = slam_util.source__from_result_model_if_parametric(
result=mass_results.last, setup_hyper=setup_hyper)
model = af.Collection(
galaxies=af.Collection(lens=mass_results.last.model.galaxies.lens,
subhalo=subhalo,
source=source),
hyper_image_sky=setup_hyper.hyper_image_sky_from_result(
result=mass_results.last, as_model=True),
hyper_background_noise=setup_hyper.hyper_background_noise_from_result(
result=mass_results.last),
)
search = af.DynestyStatic(
path_prefix=path_prefix,
name="subhalo[2]_mass[total]_source_subhalo[search_lens_plane]",
unique_tag=unique_tag,
session=session,
nlive=50,
walks=5,
facc=0.2,
)
subhalo_grid_search = af.SearchGridSearch(search=search,
number_of_steps=number_of_steps,
number_of_cores=number_of_cores)
grid_search_result = subhalo_grid_search.fit(
model=model,
analysis=analysis,
grid_priors=[
model.galaxies.subhalo.mass.centre_0,
model.galaxies.subhalo.mass.centre_1,
],
)
"""
__Model + Search + Analysis + Model-Fit (Search 3)__
In search 3 of the SUBHALO PIPELINE we refit the lens and source models above but now including a subhalo, where
the subhalo model is initalized from the highest evidence model of the subhalo grid search.
- The lens galaxy mass is modeled using MASS PIPELINE's mass distribution [Priors initialized from MASS PIPELINE].
- The source galaxy's light is parametric or an inversion depending on the previous MASS PIPELINE [Model and
priors initialized from MASS PIPELINE].
- The subhalo redshift is fixed to that of the lens galaxy.
- Each grid search varies the subhalo (y,x) coordinates and mass as free parameters.
- The priors on these (y,x) coordinates are UniformPriors, with limits corresponding to the grid-cells.
This search aims to refine the parameter estimates and errors of a dark matter subhalo detected in the grid search
above.
"""
subhalo = af.Model(al.Galaxy,
redshift=result_1.instance.galaxies.lens.redshift,
mass=subhalo_mass)
subhalo.mass.mass_at_200 = (
grid_search_result.model.galaxies.subhalo.mass.mass_at_200)
subhalo.mass.centre = grid_search_result.model.galaxies.subhalo.mass.centre
subhalo.mass.redshift_object = grid_search_result.instance.galaxies.lens.redshift
subhalo.mass.redshift_source = grid_search_result.instance.galaxies.source.redshift
model = af.Collection(
galaxies=af.Collection(
lens=grid_search_result.model.galaxies.lens,
subhalo=subhalo,
source=grid_search_result.model.galaxies.source,
),
hyper_image_sky=grid_search_result.instance.hyper_image_sky,
hyper_background_noise=grid_search_result.instance.
hyper_background_noise,
)
search = af.DynestyStatic(
name="subhalo[3]_subhalo[single_plane_refine]",
unique_tag=unique_tag,
session=session,
path_prefix=path_prefix,
nlive=100,
)
result_3 = search.fit(model=model, analysis=analysis)
return af.ResultsCollection([result_1, grid_search_result, result_3])
def with_lens_light(
path_prefix: str,
analysis: Union[al.AnalysisImaging, al.AnalysisInterferometer],
setup_hyper: al.SetupHyper,
source_results: af.ResultsCollection,
lens_bulge: af.Model(al.lp.LightProfile) = af.Model(al.lp.EllSersic),
lens_disk: af.Model(al.lp.LightProfile) = None,
lens_envelope: af.Model(al.lp.LightProfile) = None,
end_with_hyper_extension: bool = False,
unique_tag: Optional[str] = None,
session: Optional[bool] = None,
) -> af.ResultsCollection:
"""
The SlaM LIGHT PARAMETRIC PIPELINE for fitting imaging data with a lens light component.
Parameters
----------
path_prefix
The prefix of folders between the output path and the search folders.
analysis
The analysis class which includes the `log_likelihood_function` and can be customized for the SLaM model-fit.
setup_hyper
The setup of the hyper analysis if used (e.g. hyper-galaxy noise scaling).
source_results
The results of the SLaM SOURCE PARAMETRIC PIPELINE or SOURCE INVERSION PIPELINE which ran before this pipeline.
lens_bulge
The `LightProfile` `Model` used to represent the light distribution of the lens galaxy's bulge (set to
None to omit a bulge).
lens_disk
The `LightProfile` `Model` used to represent the light distribution of the lens galaxy's disk (set to
None to omit a disk).
lens_envelope
The `LightProfile` `Model` used to represent the light distribution of the lens galaxy's envelope (set to
None to omit an envelope).
end_with_hyper_extension
If `True` a hyper extension is performed at the end of the pipeline. If this feature is used, you must be
certain you have manually passed the new hyper images geneted in this search to the next pipelines.
unique_tag
The unique tag for this model-fit, which will be given a unique entry in the sqlite database and also acts as
the folder after the path prefix and before the search name. This is typically the name of the dataset.
"""
"""
__Model + Search + Analysis + Model-Fit (Search 1)__
In search 1 of the LIGHT PARAMETRIC PIPELINE we fit a lens model where:
- The lens galaxy light is modeled using parametric bulge + disk + envelope [no prior initialization].
- The lens galaxy mass is modeled using SOURCE PIPELINE's mass distribution [Parameters fixed from MASS PIPELINE].
- The source galaxy's light is modeled using SOURCE PIPELINE's model [Parameters fixed from SOURCE PIPELINE].
This search aims to produce an accurate model of the lens galaxy's light, which may not have been possible in the
SOURCE PIPELINE as the mass and source models were not properly initialized.
"""
"""
If hyper-galaxy noise scaling for the lens is on, it may have scaled the noise to high values in the SOURCE
PIPELINE (which fitted a simpler lens light model than this pipeline). The new lens light model fitted in this
pipeline may fit the data better, requiring a reducing level of noise scaling. For this reason, the noise scaling
normalization is included as a free parameter.
"""
hyper_galaxy = setup_hyper.hyper_galaxy_lens_from_result(
result=source_results.last, noise_factor_is_model=True)
source = slam_util.source__from_result(result=source_results.last,
setup_hyper=setup_hyper,
source_is_model=False)
model = af.Collection(
galaxies=af.Collection(
lens=af.Model(
al.Galaxy,
redshift=source_results.last.instance.galaxies.lens.redshift,
bulge=lens_bulge,
disk=lens_disk,
envelope=lens_envelope,
mass=source_results.last.instance.galaxies.lens.mass,
shear=source_results.last.instance.galaxies.lens.shear,
hyper_galaxy=hyper_galaxy,
),
source=source,
),
hyper_image_sky=setup_hyper.hyper_image_sky_from_result(
result=source_results.last, as_model=True),
hyper_background_noise=setup_hyper.hyper_background_noise_from_result(
result=source_results.last),
)
search = af.DynestyStatic(
path_prefix=path_prefix,
name="light[1]_light[parametric]",
unique_tag=unique_tag,
session=session,
nlive=75,
)
result_1 = search.fit(model=model, analysis=analysis.no_positions)
"""
__Hyper Extension__
The above search is extended with a hyper-search if the SetupHyper has one or more of the following inputs:
- The source is using an `Inversion`.
- One or more `HyperGalaxy`'s are included.
- The background sky is included via `hyper_image_sky` input.
- The background noise is included via the `hyper_background_noise`.
"""
if end_with_hyper_extension:
result_1 = extensions.hyper_fit(
setup_hyper=setup_hyper,
result=result_1,
analysis=analysis,
include_hyper_image_sky=True,
)
return af.ResultsCollection([result_1])
def no_lens_light(
settings_autofit: slam_util.SettingsAutoFit,
analysis: Union[al.AnalysisImaging, al.AnalysisInterferometer],
setup_hyper: al.SetupHyper,
source_parametric_results: af.ResultsCollection,
pixelization: af.Model(al.pix.Pixelization) = af.Model(
al.pix.VoronoiBrightnessImage),
regularization: af.Model(al.reg.Regularization) = af.Model(
al.reg.Constant),
) -> af.ResultsCollection:
"""
The S:aM SOURCE INVERSION PIPELINE for fitting imaging data without a lens light component.
Parameters
----------
analysis
The analysis class which includes the `log_likelihood_function` and can be customized for the SLaM model-fit.
setup_hyper
The setup of the hyper analysis if used (e.g. hyper-galaxy noise scaling).
source_parametric_results
The results of the SLaM SOURCE PARAMETRIC PIPELINE which ran before this pipeline.
pixelization
The pixelization used by the `Inversion` which fits the source light.
regularization
The regularization used by the `Inversion` which fits the source light.
"""
"""
__Model + Search + Analysis + Model-Fit (Search 1)__
In search 1 of the SOURCE INVERSION PIPELINE we fit a lens model where:
- The lens galaxy mass is modeled using a total mass distribution [parameters fixed to result of SOURCE PARAMETRIC
PIPELINE].
- The source galaxy's light is a `VoronoiMagnification` pixelization and `Constant` regularization scheme.
This search aims to quickly estimate values for the pixelization resolution and regularization coefficient.
"""
model = af.Collection(
galaxies=af.Collection(
lens=af.Model(
al.Galaxy,
redshift=source_parametric_results.last.instance.galaxies.lens.
redshift,
mass=source_parametric_results.last.instance.galaxies.lens.
mass,
shear=source_parametric_results.last.instance.galaxies.lens.
shear,
),
source=af.Model(
al.Galaxy,
redshift=source_parametric_results.last.instance.galaxies.
source.redshift,
pixelization=al.pix.VoronoiMagnification,
regularization=al.reg.Constant,
hyper_galaxy=setup_hyper.hyper_galaxy_source_from_result(
result=source_parametric_results.last),
),
),
hyper_image_sky=setup_hyper.hyper_image_sky_from_result(
result=source_parametric_results.last, as_model=False),
hyper_background_noise=setup_hyper.hyper_background_noise_from_result(
result=source_parametric_results.last),
)
search = af.DynestyStatic(
path_prefix=settings_autofit.path_prefix,
name=
"source_inversion[1]_mass[fixed]_source[inversion_magnification_initialization]",
unique_tag=settings_autofit.unique_tag,
number_of_cores=settings_autofit.number_of_cores,
session=settings_autofit.session,
nlive=30,
)
result_1 = search.fit(model=model,
analysis=analysis.no_positions,
info=settings_autofit.info)
"""
__Model + Search + Analysis + Model-Fit (Search 2)__
In search 2 of the SOURCE INVERSION PIPELINE we fit a lens model where:
- The lens galaxy mass is modeled using a total mass distribution [parameters initialized from the results of the
SOURCE PARAMETRIC PIPELINE].
- The source galaxy's light is a `VoronoiMagnification` pixelization and `Constant` regularization scheme
[parameters are fixed to the result of search 1].
This search aims to improve the lens mass model using the search 1 `Inversion`.
"""
model = af.Collection(
galaxies=af.Collection(
lens=af.Model(
al.Galaxy,
redshift=result_1.instance.galaxies.lens.redshift,
mass=source_parametric_results.last.model.galaxies.lens.mass,
shear=source_parametric_results.last.model.galaxies.lens.shear,
),
source=af.Model(
al.Galaxy,
redshift=result_1.instance.galaxies.source.redshift,
pixelization=result_1.instance.galaxies.source.pixelization,
regularization=result_1.instance.galaxies.source.
regularization,
hyper_galaxy=result_1.instance.galaxies.source.hyper_galaxy,
),
),
hyper_image_sky=result_1.instance.hyper_image_sky,
hyper_background_noise=result_1.instance.hyper_background_noise,
)
search = af.DynestyStatic(
path_prefix=settings_autofit.path_prefix,
name="source_inversion[2]_mass[total]_source[fixed]",
unique_tag=settings_autofit.unique_tag,
number_of_cores=settings_autofit.number_of_cores,
session=settings_autofit.session,
nlive=50,
)
result_2 = search.fit(model=model,
analysis=analysis,
info=settings_autofit.info)
"""
__Model + Search + Analysis + Model-Fit (Search 3)__
In search 3 of the SOURCE INVERSION PIPELINE we fit a lens model where:
- The lens galaxy mass is modeled using a total mass distribution [parameters fixed to result of search 2].
- The source galaxy's light is the input pixelization and regularization.
This search aims to estimate values for the pixelization and regularization scheme.
"""
model = af.Collection(
galaxies=af.Collection(
lens=af.Model(
al.Galaxy,
redshift=result_2.instance.galaxies.lens.redshift,
mass=result_2.instance.galaxies.lens.mass,
shear=result_2.instance.galaxies.lens.shear,
),
source=af.Model(
al.Galaxy,
redshift=result_2.instance.galaxies.source.redshift,
pixelization=pixelization,
regularization=regularization,
hyper_galaxy=result_2.instance.galaxies.source.hyper_galaxy,
),
),
hyper_image_sky=result_2.instance.hyper_image_sky,
hyper_background_noise=result_2.instance.hyper_background_noise,
)
search = af.DynestyStatic(
path_prefix=settings_autofit.path_prefix,
name="source_inversion[3]_mass[fixed]_source[inversion_initialization]",
unique_tag=settings_autofit.unique_tag,
number_of_cores=settings_autofit.number_of_cores,
session=settings_autofit.session,
nlive=30,
dlogz=10.0,
sample="rstagger",
)
analysis.set_hyper_dataset(result=result_2)
result_3 = search.fit(model=model,
analysis=analysis.no_positions,
info=settings_autofit.info)
result_3.use_as_hyper_dataset = True
"""
__Model + Search + Analysis + Model-Fit (Search 4)__
In search 4 of the SOURCE INVERSION PIPELINE we fit a lens model where:
- The lens galaxy mass is modeled using a total mass distribution [parameters initialized from the results of the
search 2].
- The source galaxy's light is the input pixelization and regularization scheme [parameters fixed to the result
of search 3].
This search aims to improve the lens mass model using the input `Inversion`.
"""
mass = slam_util.mass__from_result(
mass=result_2.model.galaxies.lens.mass,
result=source_parametric_results.last,
unfix_mass_centre=True,
)
model = af.Collection(
galaxies=af.Collection(
lens=af.Model(
al.Galaxy,
redshift=result_3.instance.galaxies.lens.redshift,
mass=mass,
shear=result_2.model.galaxies.lens.shear,
),
source=af.Model(
al.Galaxy,
redshift=result_3.instance.galaxies.source.redshift,
pixelization=result_3.instance.galaxies.source.pixelization,
regularization=result_3.instance.galaxies.source.
regularization,
hyper_galaxy=result_3.instance.galaxies.source.hyper_galaxy,
),
),
hyper_image_sky=result_3.instance.hyper_image_sky,
hyper_background_noise=result_3.instance.hyper_background_noise,
)
analysis.preloads = al.Preloads.setup(result=result_3,
model=model,
pixelization=True)
search = af.DynestyStatic(
path_prefix=settings_autofit.path_prefix,
name="source_inversion[4]_mass[total]_source[fixed]",
unique_tag=settings_autofit.unique_tag,
number_of_cores=settings_autofit.number_of_cores,
session=settings_autofit.session,
nlive=50,
)
result_4 = search.fit(model=model,
analysis=analysis,
info=settings_autofit.info)
"""
__Hyper Extension__
The above search is extended with a hyper-search if the SetupHyper has one or more of the following inputs:
- The source is using an `Inversion`.
- One or more `HyperGalaxy`'s are included.
- The background sky is included via `hyper_image_sky` input.
- The background noise is included via the `hyper_background_noise`.
"""
result_4 = extensions.hyper_fit(
setup_hyper=setup_hyper,
result=result_4,
analysis=analysis,
include_hyper_image_sky=True,
)
return af.ResultsCollection([result_1, result_2, result_3, result_4])