Title: Phase correction for ALMA - Investigating water vapour radiometer scaling:The long-baseline science verification data case study,
Abstract: The Atacama Large millimetre/submillimetre Array (ALMA) makes use of water
vapour radiometers (WVR), which monitor the atmospheric water vapour line at
183 GHz along the line of sight above each antenna to correct for phase delays
introduced by the wet component of the troposphere. The application of WVR
derived phase corrections improve the image quality and facilitate successful
observations in weather conditions that were classically marginal or poor. We
present work to indicate that a scaling factor applied to the WVR solutions can
act to further improve the phase stability and image quality of ALMA data. We
find reduced phase noise statistics for 62 out of 75 datasets from the
long-baseline science verification campaign after a WVR scaling factor is
applied. The improvement of phase noise translates to an expected coherence
improvement in 39 datasets. When imaging the bandpass source, we find 33 of the
39 datasets show an improvement in the signal-to-noise ratio (S/N) between a
few to ~30 percent. There are 23 datasets where the S/N of the science image is
improved: 6 by <1%, 11 between 1 and 5%, and 6 above 5%. The higher frequencies
studied (band 6 and band 7) are those most improved, specifically datasets with
low precipitable water vapour (PWV), <1mm, where the dominance of the wet
component is reduced. Although these improvements are not profound, phase
stability improvements via the WVR scaling factor come into play for the higher
frequency (>450 GHz) and long-baseline (>5km) observations. These inherently
have poorer phase stability and are taken in low PWV (<1mm) conditions for
which we find the scaling to be most effective. A promising explanation for the
scaling factor is the mixing of dry and wet air components, although other
origins are discussed. We have produced a python code to allow ALMA users to
undertake WVR scaling tests and make improvements to their data.