mimical 0.2.8

Intensity modelling for multiply-imaged objects

Mimical (Modelling the Intensity of Multiply-Imaged Celestial Ancient Light)

Mimical is an intensity modelling code for multiply-imaged objects, performing simultaenous Bayseian inference of model parameters via the nested sampling algorithm. Mimical supports any astropy 2D model, and supports user defined parameter polynomial 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.

4. mimical_prior - The Mimical prior

Mimical prior

Below is an example mimical_prior for a run using the default astropy sersic model. The first set of element keys must match the astropy model 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 flux_to_counts. The rms parameter traces the RMS noise in the image; this can be fit with Mimical but it is highly recommended to provide it to reduce dimensionality , ideally as a list of arrays of the same shape as images or failing that as a list of floats (see Fixing parameters). This is likewise for flux_to_counts, which helps Mimical calculate the poisson uncertainty associated with the generated model; this can be easily provided by the user with information on the gain and exposure time.

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'] = ((0,1), 'Individual')

mimical_prior['flux_to_counts'] = ((1,1e6), 'Individual')

Optional input and parameters

• astropy_model = Sersic2D() - Any astropy 2D model

• pool = None - Number of cores to parallelise likelihood calculations to

• sampler = 'Nautilus' - The nested sampler to use, other options include Dynesty

• oversample_boxlength = 15 - Length of box in the centre of the image to perform oversampling in

• oversample_factor = 10 - Factor by which to oversample inside the above box

• sextractor_clean = False - Whether or not to let Sextractor clean the images of other objects

• sextractor_target_maxdistancepix='default' - Radius from the image centre at which Sextractor discards the closest object as contamination. Needed for when the target object is undetected.

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 ('Polynomial', 0). Or, to supply the RMS for each image separately, one would pass a list of length N_filters and choose the options (Individual). If the user supplies arrays for RMS and flux-to-counts 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.