prefactor

The LOFAR pre-facet calibration pipeline.

prefactor is a pipeline to correct for various instrumental and ionospheric effects in both LOFAR HBA and LOFAR LBA observations. It will prepare your data so that you will be able to use any direction-dependent calibration software, like factor or killMS.

It includes:

• removal of clock offsets between core and remote stations (using clock-TEC separation)
• correction of the polarization alignment between XX and YY
• robust time-independent bandpass correction
• ionospheric RM corrections with RMextract
• removal of the element beam
• advanced flagging and interpolation of bad data
• mitigation of broad-band RFI and bad stations
• direction-independent phase correction of the target, using a global sky model from TGSS ADR or the new Global Sky Model GSM
• detailled diagnostics
• (optional) wide-band cleaning in Initial-Subtract and Pre-Facet-Image

The full documentation can be found at the prefactor webpage.

Software requirements:

• the full "offline" LOFAR software installation (version >= 3.1)
• LoSoTo (version >= 2.0)
• LSMTool (version >= 1.4)
• RMextract
• Python (including matplotlib, scipy, and astropy)
• AOFlagger
• WSClean (for Initial-Subtract; version >= 2.5)
• for Initial-Subtract-IDG(-LowMemory).parset and Pre-Facet-Image.parset: WSClean must be compiled with IDG
• APLpy (for Initial-Subtract and Pre-Facet-Image)

Installation

The recommended way to install prefactor is to download it from github with:

git clone https://github.com/lofar-astron/prefactor.git

This allows for easy updating of the code to include bugfixes or new features. It is also possible to download tar files of releases from the release page.

Once downloaded, the installation is complete; to set up a run, see the detailed setup information at the prefactor webpage.

Directory Structure

prefactor contains the following sub-directories:

• bin scripts for your convenience. At the moment, these consist only of a state-file manipulation tool for changing the state of a previously run pipeline
• plugins scripts for manipulating mapfiles
• rfistrategies strategies for statistical RFI mitigation using AOFlagger
• scripts scripts that the pipeline calls to process data, generate plots, etc.
• skymodels skymodels that are used by the pipeline (e.g. for demixing or calibrating the calibrator)

The main directory contains the different parsets for the genericpipeline:

• Pre-Facet-Calibrator.parset : The calibrator part of the "standard" pre-facet calibration pipeline.
• Pre-Facet-Target.parset : The target part of the "standard" pre-facet calibration pipeline.
• Concatenate.parset : A pipeline that concatenates single-subband target data to produce concatenated bands suitable for the initial-subtract pipeline.
• Initial-Subtract.parset : A pipeline that generates full-FoV images and subtracts the sky-models from the visibilities. (Needed for facet-calibration.)
• Initial-Subtract-IDG.parset : Same as Initial-Subtract-Fast.parset, but uses the image domain gridder (IDG) in WSClean.
• Initial-Subtract-IDG-LowMemory.parset : Same as Initial-Subtract-Fast.parset, but uses the image domain gridder (IDG) in WSClean for high-res imaging.
• Pre-Facet-Image.parset : A pipeline that generates a full-bandwidth, full-FoV image.
• make_calibrator/target_plots.losoto_parset : Losoto parsets for making diagnostic plots from the output h5parms.

The Pre-Facet-Calibration pipeline and its scripts where developed by:

• Alexander Drabent
• David Rafferty
• Andreas Horneffer
• Francesco de Gasperin
• Marco Iacobelli
• Emanuela Orru
• Björn Adebahr
• Martin Hardcastle
• George Heald
• Soumyajit Mandal
• Carole Roskowinski
• Jose Sabater Montes
• Timothy Shimwell
• Sarrvesh Sridhar
• Reinout van Weeren
• Wendy Williams

With special thanks to Stefan Fröhlich for developing the genericpipeline.

Acknowledgement

Prefactor3 procedure is described in this paper:

• de Gasperin, F.; Dijkema, T. J.; Drabent, A.; Mevius, M.; Rafferty, van Weeren, R., et al. 2019, A&A, 662, A5

Fator procedure is described in these papers:

• van Weeren, R. J., Williams, W. L., Hardcastle, M. J., et al. 2016, ApJS, 223, 2
• Williams, W. L., van Weeren, R. J., Röttgering, H. J. A., et al. 2016, MNRAS, 460, 2385W