Chimenea and other tools: Automated imaging of multi-epoch radio-synthesis data with CASA

摘要

In preparing the way for the Square Kilometre Array and its pathfinders,there is a pressing need to begin probing the transient sky in a fully roboticfashion using the current generation of radio telescopes. Effectiveexploitation of such surveys requires a largely automated data-reductionprocess. This paper introduces an end-to-end automated reduction pipeline,AMIsurvey, used for calibrating and imaging data from the Arcminute MicrokelvinImager Large Array. AMIsurvey makes use of several component libraries whichhave been packaged separately for open-source release. The most scientificallysignificant of these is chimenea, which implements a telescope-agnosticalgorithm for automated imaging of pre-calibrated multi-epoch radio-synthesisdata, of the sort typically acquired for transient surveys or follow-up. Thealgorithm aims to improve upon standard imaging pipelines by utilizingiterative RMS-estimation and automated source-detection to avoid so called`Clean-bias', and makes use of CASA subroutines for the underlyingimage-synthesis operations. At a lower level, AMIsurvey relies upon twolibraries, drive-ami and drive-casa, built to allow use of matureradio-astronomy software packages from within Python scripts. While targeted atautomated imaging, the drive-casa interface can also be used to automateinteraction with any of the CASA subroutines from a generic Python process.Additionally, these packages may be of wider technical interest beyondradio-astronomy, since they demonstrate use of the Python library pexpect toemulate terminal interaction with an external process. This approach allows forrapid development of a Python interface to any legacy or externally-maintainedpipeline which accepts command-line input, without requiring alterations to theoriginal code.