mimical 0.2.2

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 - 3D image array with dimensions (N_filters, N_y, N_x)

2. filt_list - An array of paths to filters curves of dimension N_filters

3. psfs - 3D PSF array with dimensions (N_filters, M_y, M_x)

4. mimical_prior - A 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 (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, 10*images.max()), 'Individual')

mimical_prior['r_eff'] = ((0, images.shape[2]), 'Polynomial', 1)

mimical_prior['n'] = ((0.1, 10), 'Polynomial', 1)

mimical_prior['x_0'] = ((images.shape[2]/2-2, images.shape[2]/2+2), 'Polynomial', 0)

mimical_prior['y_0'] = ((images.shape[1]/2-2, images.shape[1]/2+2), '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 / ndarray. 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/ndarray of length N_filters and choose the options (Individual).

Workflow diagram