mimical 0.3.2

Intensity modelling for multiply-imaged objects

Mimical (Modelling the Intensity of Multiply-Imaged CelestiAl Light)

Mimical is an intensity modelling code for multiply-imaged objects, performing simultaenous Bayseian inference of model parameters via the nested sampling algorithm. Mimical currently uses a built-in Sersic submodel, but users can easily bolt-on any other 2D submodel. Mimical supports user defined parameter polynomial or power-law depenency with image wavelength.

Installation

Mimical can be installed with pip:

pip install mimical

Required input

1. images - An image array or list of image arrays with elements for each filter.

2. filt_list - A path string or list of path strings to the filter transmission curve files.

3. psfs - A PSF image array or list of PSF image arrays with elements for each filter. (Normalised to 1)

4. mimical_prior - The Mimical prior

Mimical prior

Below is an example mimical_prior for a run using the default Sersic submodel. The first set of element keys must match the submodel parameter names. Following this, the next element, named psf_pa, traces the rotation of the PSF. The final two elements must be named rms and counts_per_flux.

mimical_prior = {}

mimical_prior['amplitude'] = ((0, 1), 'Individual')
mimical_prior['r_eff'] = ((0, 50), 'Polynomial', 1)
mimical_prior['n'] = ((0.1, 10), 'Polynomial', 1)
mimical_prior['x_0'] = ((48, 52), 'Polynomial', 0)
mimical_prior['y_0'] = ((48, 52), 'Polynomial', 0)
mimical_prior['ellip'] = ((0,0.75), 'Polynomial', 0)
mimical_prior['theta'] = ((0, np.pi), 'Polynomial', 0)

mimical_prior['psf_pa'] = ((-180, 180), 'Polynomial', 0)

mimical_prior['rms'] = ('Infer',)``
mimical_prior['counts_per_flux'] = (cpf_list, 'Individual')

The rms parameter traces the RMS noise in the image; this can be fit with Mimical but it is highly recommended to fix it in order to reduce dimensionality (see Fixing parameters), either by passing it in as a float / array / list of floats / list of arrays of the same length and shape as images, or by selecting the special Mimical prior type 'Infer' which automatically calculates the RMS of the image background as identified by SourceExtractor.

Similarly for counts_per_flux, which allows Mimical to associate poisson uncertainty with the generated model, it is recommended to fix it to a provided quantity (float / array / list of floats / list of arrays of the same length and shape as images). This may be challenging to derive, but can be provided by the user with information on the gain, exposure time, etc.

Optional input and parameters

• submodel : 2D submodel used to model the underlying intensity profile.

• pool : Number of cores to parallelise likelihood calculations to.

• oversample_boxlength : Width of box about image center to oversample within.

• oversample_factor : Factor by which to oversample the central box.

• sextractor_clean : Whether or not to let sextractor clean the input images of contaminants.

• sextractor_target_maxdistancepix : The distance after which the closest detected source is considered a contaminant. Necessary for images in which the target is undetected.

• dilute : Whether or not to apply a circular miminum filter over the contamination map to dilute it.

• dilute_radius : If dilute is ‘True’, apply minimim filter with radius ‘dilute_radius’ over the contamination map.

• oversample : Oversample factor for the entire image or annuli defined by oversample radii.

• oversample_radii : Radii in which to oversample.

Fixing parameters

You can fix any of the parameters in the Mimical prior by setting the first element in the parameter tuple equal to either a float / int / list. For instance, to keep x_0 constant across all images, one would pass a float/int and choose the options (float/int, 'Polynomial', 0). Or, to supply the RMS for each image separately, one would pass a list of length N_filters and choose the options (list, 'Individual'). If the user supplies arrays for RMS and counts-per-flux in the same shape as the images, the corner plot samples will show the mean of these images for generality, but the full arrays will be parsed in the likelihood function.

Parallelisation

Mimical can be parallelised to different cores in one of two ways:

• The likelihood calculations can be parallelised to different cores by using the pool keyword argument. This is ideal for single object fits.

• When using fit_catalogue, the mpi_serial keyword arguement can be set to True for individual object fits to be parallelised to separate cores. With this option enabled, mimical must be run using mpirun/mpiexec -n [ncores] python [filename].py. This is ideal for large catalogue fits.

Running Mimical with both of these options enabled is untested.