jaxtronomy.Sampling.Likelihoods package

Submodules

jaxtronomy.Sampling.Likelihoods.image_likelihood module

class ImageLikelihood(multi_band_list, multi_band_type, kwargs_model, bands_compute=None, image_likelihood_mask_list=None, source_marg=False, linear_prior=None, check_positive_flux=False, kwargs_pixelbased=None, linear_solver=True)

Bases: object

Manages imaging data likelihoods.

__init__(multi_band_list, multi_band_type, kwargs_model, bands_compute=None, image_likelihood_mask_list=None, source_marg=False, linear_prior=None, check_positive_flux=False, kwargs_pixelbased=None, linear_solver=True)

Parameters:

• multi_band_list – list of imaging band configurations [[kwargs_data, kwargs_psf, kwargs_numerics],[…], …]

• multi_band_type – string, can be “single-band” only in jaxtronomy. “multi-linear” and “joint-liner” not supported yet

• kwargs_model – dict containing model option keyword arguments. See arguments to class_creator.create_class_instances() for options.

• bands_compute – bool list to indicate which band to be included in the modeling, only relevant for joint-linear and multi-linear. For single-band, the band index is zero by default.

• image_likelihood_mask_list – list of boolean 2d arrays of size of images marking the pixels to be evaluated in the likelihood

• source_marg – bool, performs a marginalization over the linear parameters

• linear_prior – linear prior width in eigenvalues

• check_positive_flux – checks whether the results of the linear solver amplitudes are positive, and applies a logL penalty if amplitudes are negative (for profiles whose amplitudes have a physical interpretation)

• kwargs_pixelbased – should be None; not supported in jaxtronomy

• linear_solver – bool, determines whether to solve for linear parameters (amplitudes of sources)

logL(kwargs_lens=None, kwargs_source=None, kwargs_lens_light=None, kwargs_ps=None, kwargs_special=None, kwargs_extinction=None, **kwargs)

Computes the log likelihood of the data given a model. The model kwargs are used to simulate an image which is compared to the data image.

Parameters:

• kwargs_lens – lens model keyword argument list according to LensModel module

• kwargs_source – source light keyword argument list according to LightModel module

• kwargs_lens_light – deflector light (not lensed) keyword argument list according to LightModel module

• kwargs_ps – point source keyword argument list according to PointSource module

• kwargs_special – special keyword argument list as part of the Param module

• kwargs_extinction – extinction parameter keyword argument list according to LightModel module

Returns:

log likelihood of the data given the model, linear parameter inversion list

property num_data

Returns:

number of image data points

num_param_linear(kwargs_lens=None, kwargs_source=None, kwargs_lens_light=None, kwargs_ps=None, kwargs_special=None, kwargs_extinction=None, kwargs_tracer_source=None)

Returns:

number of linear parameters solved for during the image reconstruction process

jaxtronomy.Sampling.Likelihoods.position_likelihood module

class PositionLikelihood(point_source_class, image_position_uncertainty=0.005, astrometric_likelihood=False, image_position_likelihood=False, ra_image_list=None, dec_image_list=None, source_position_likelihood=None, source_position_tolerance=None, source_position_sigma=0.001, force_no_add_image=False, restrict_image_number=False, max_num_images=None)

Bases: object

Likelihood of positions of multiply imaged point sources.

__init__(point_source_class, image_position_uncertainty=0.005, astrometric_likelihood=False, image_position_likelihood=False, ra_image_list=None, dec_image_list=None, source_position_likelihood=None, source_position_tolerance=None, source_position_sigma=0.001, force_no_add_image=False, restrict_image_number=False, max_num_images=None)

Parameters:

• point_source_class – Instance of PointSource() class

• image_position_uncertainty – uncertainty in image position uncertainty (1-sigma Gaussian radially), this is applicable for astrometric uncertainties as well as if image positions are provided as data

• astrometric_likelihood – bool, if True, evaluates the astrometric uncertainty of the predicted and modeled image positions with an offset ‘delta_x_image’ and ‘delta_y_image’

• image_position_likelihood – bool, if True, evaluates the likelihood of the model predicted image position given the data/measured image positions

• ra_image_list – list of lists; RA image positions per model component

• dec_image_list – list of lists; DEC image positions per model component

• source_position_likelihood – bool, if True, ray-traces image positions back to source plane and evaluates relative errors in respect ot the position_uncertainties in the image plane (image_position_uncertainty)

• source_position_tolerance (None or float) – tolerance level (in arc seconds in the source plane) of the different images. If set =! None, then the backwards ray tracing is performed on the images and demand on the same position of the source is meant to match the requirements, otherwise a punishing likelihood term is introduced

• source_position_sigma – r.m.s. value corresponding to a 1-sigma Gaussian likelihood accepted by the model precision in matching the source position transformed from the image plane

• force_no_add_image – bool, if True, will punish additional images appearing in the frame of the modelled image(first calculate them)

• restrict_image_number – bool, if True, searches for all appearing images in the frame of the data and compares with max_num_images

• max_num_images – integer, maximum number of appearing images. Default is the number of images given in the Param() class

logL(kwargs_lens, kwargs_ps, kwargs_special, verbose=False)

Parameters:

• kwargs_lens – lens model parameter keyword argument list

• kwargs_ps – point source model parameter keyword argument list

• kwargs_special – special keyword arguments

• verbose – bool

Returns:

log likelihood of the optional likelihoods being computed

static astrometric_likelihood(kwargs_ps, kwargs_special, sigma)

Evaluates the astrometric uncertainty of the model plotted point sources (only available for ‘LENSED_POSITION’ point source model) and predicted image position by the lens model including an astrometric correction term.

Parameters:

• kwargs_ps – point source model kwargs list

• kwargs_special – kwargs list, should include the astrometric corrections ‘delta_x’, ‘delta_y’

• sigma – 1-sigma Gaussian uncertainty in the astrometry

Returns:

log likelihood of the astrometirc correction between predicted image positions and model placement of the point sources

image_position_likelihood(kwargs_ps, kwargs_lens, sigma)

Computes the likelihood of the model predicted image position relative to measured image positions with an astrometric error. This routine requires the ‘ra_image_list’ and ‘dec_image_list’ being declared in the initiation of the class.

Parameters:

• kwargs_ps – point source keyword argument list

• kwargs_lens – lens model keyword argument list

• sigma – 1-sigma uncertainty in the measured position of the images

Returns:

log likelihood of the model predicted image positions given the data/measured image positions.

source_position_likelihood(kwargs_lens, kwargs_ps, sigma, hard_bound_rms=None, verbose=False)

Computes a likelihood/punishing factor of how well the source positions of multiple images match given the image position and a lens model. The likelihood level is computed in respect of a displacement in the image plane and transposed through the Hessian into the source plane.

Parameters:

• kwargs_lens – lens model keyword argument list

• kwargs_ps – point source keyword argument list

• sigma – float, 1-sigma Gaussian uncertainty in the image plane

• hard_bound_rms – float or None, hard bound deviation between the mapping of the images back to the source plane (in source frame)

• verbose – unused

Returns:

log likelihood of the model reproducing the correct image positions given an image position uncertainty

property num_data

Returns:

integer, number of data points associated with the class instance

Module contents