Calibration and Imaging of Radio Interferometric Data¶

The calim package deals with the calibration and imaging of radio interferometry data. Although it is a subpackage within iLiSA it is fairly independent of LOFAR data.

The main tasks in this package is the calibration of visibility data and the imaging of visibility data. Calibration is the determination of the antenna gains and their application to measured data, while imaging entails computing a brightness distribution, or map of the sky, based on measured visibilites.

calim consists of the following subpackages:

Sky Models¶

With the skymodels module, you can create an empirically based model of the radio sky above a geographical location for a given time and for a range of frequencies. The models are generated using PyGDSM, which are sky maps, and from these maps, model visibilities matching a given array can be generated. These model visibilites can then be used as a reference for the calibration of measured visibility data (see Calibration Section, on how to calibrate).

The module’s CLI is run by providing it with the desired output representation and an XST data file-folder. Output representation can be either ‘vis’ for visibility data or ‘img’ to plot a image of the sky model corresponding to the observational conditions of the input XST data. So for example:

[localhost]$ calim_model vis SE607_20220222_093955_spw3_sb230_int1_dur30_dirCas-A_xst
[localhost]$ ls -d *mod_xst
SE607_20220222_093955_spw3_sb230_int1_dur30_dirCas-A_mod_xst

results in a new data file-folder with presuffix _mod_, signifying a model dataset based on the dataset without this presuffix.

Calibration¶

The calibration module allows users to solve for and apply, gain solutions when calibrating visibility data.

The first step of calibration is to find the gains of the antennas that best matches the observed data with a model of the data. This is known as solving (argument sol) for the gains. E.g.

[localhost]$ calim_calib sol SE607_20220222_093955_spw3_sb230_int1_dur30_dirCas-A_xst
[localhost]$ ls -d *mod_xst/*.npy
gainsolutions.npy

Here the file-folder of the measured data is provided and the model data is assumed to be in the corresponding ‘_mod_’ file-folder and the resulting gain solutions are saved in a file in the ‘_mod_’ file-folder.

The final step in calibration is the application of the gain solutions to the measured data to obtain calibrated data. E.g.

[localhost]$ calim_calib app SE607_20220222_093955_spw3_sb230_int1_dur30_dirCas-A_xst
[localhost]$ ls -d *cal_xst/
SE607_20220222_093955_spw3_sb230_int1_dur30_dirCas-A_cal_xst

Thus, in the application step the argument app results in a new dataset file-folder with presuffix _cal_, which is the final, calibrated visibiilty dataset.

One can combine the two steps above such that they executed one directly after the other by given the argument sol:app.

Visibilities¶

The visibilities module handles visibility data, that is the correlated measurements of antenna arrays.

Imaging¶

Imaging functionality is contained in the imaging module.

The main CLI script provided by this module is ilisa_image. It has multiple imaging algorithms which one choose to apply to visibility datasets. It has a classic beamform imaging algorithm (argument bf) and also a near-field imager (argument nf). The former makes sky images while the latter makes images in the tangent plane of the station, which is useful for RFI source location.

Examples¶

Beamform imaging:

[localhost]$ calim_image bf SE607_20220222_093955_spw3_sb230_int1_dur30_dirCas-A_xst

results in an image such as:

Example ilisa_image image. — Polarimetric sky maps from SE607’s LBA station.¶

which shows an example beamformed (argument bf) sky map imaged on-the-fly from the given visibility file.