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Multi Source Self Calibration

Multi-Source Self-Calibration (MSSC) is a direction-dependent, calibration technique which provides an additional step to standard phase referencing. MSSC uses multiple faint sources detected within the primary beam and combines them together. The combined response of many sources across the field-of-view is more than sufficient to allow phase corrections to be derived. Each source have their CLEAN models divided into the visibilities which results in multiple point sources. These are stacked in the uv plane to increase the S/N, permitting self-calibration to become feasible. It is worth noting that this process only applies to wide-field VLBI data sets that detect and image multiple sources within one epoch. Recent improvements in the capabilities of VLBI correlators is ensuring that wide-field VLBI is a reality and as a result there will be an increased number of experiments which can utilise MSSC. If MSSC is used, please reference Radcliffe et al. (2016)

Pre-requisites: Parseltongue, python 2.7 & AIPS

Usage: Sources which have been split and averaged to an individual file per source. After changing the input file, MSSC can be run on using the following command:

ParselTongue multi_source_self_cal.py MSSC_input.txt

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About

Multi-Source Self-Calibration is a Parseltongue (Kettenis et al. 2012) script which provides an extra calibration for VLBI data sets that image multiple target source within one observation. More information is in the README & paper Radcliffe et. al 2016. If you use this script please reference Radcliffe et al. 2016 which is the paper that accom…

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