Serial MultiView: an efficient approach to mitigating atmospheric spatial-structure errors for VLBI astrometry

Abstract

Atmospheric propagation errors are a main constraint on the accuracy of Very Long Baseline Interferometry (VLBI) astrometry. For relative astrometry, differential techniques can mitigate these errors, but their effectiveness diminishes with decreasing elevation and increasing angular separations between target and calibrator, among others. The MultiView technique addresses atmospheric spatial-structure errors by observing multiple calibrators around the target and interpolating at the target position, thereby reducing atmospheric errors more effectively than phase-referencing with only one calibrator. The first MultiView realisation at 1.6GHz involved cyclically observing all calibrators and the target, fitting a phase plane from calibrator solutions in each cycle, and is a well-established technique. This implementation reduces on-target time and is constricted by the short atmospheric coherence time at high frequencies. We propose a new realisation, serial MultiView, which rotates the phase plane iteratively based on the time series of calibrator residual phases. This new strategy obviates the necessity of observing all calibrators within each cycle, thereby shortening the observing cycle and offering considerable potential at higher frequencies where the temporal structure is the dominant source of errors. Additionally, by incorporating time-domain information in the iterations, phase ambiguities can be accurately and automatically identified. We verify the astrometric accuracy of serial MultiView at 5GHz by comparing it to conventional MultiView, achieving <10uas error in RA direction, and show the calibration overhead can be reduced in both approaches. This approach enables efficient, high-accuracy differential astrometry and artifact-reduced imaging for astrophysical studies, and we provide a user-friendly tool for it.

Accepted for publication in AJ

References in corpus (16)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Array Programming with NumPy
• Astropy: A Community Python Package for Astronomy
• The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package
• The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package
• Gaia Data Release 3: Summary of the content and survey properties
• Trigonometric Parallaxes of Massive Star Forming Regions: I. S 252 & G232.6+1.0
• The Measurement of the Light Deflection from Jupiter: Experimental Results
• Precise radio astrometry and new developments for the next generation of instruments
• Characterizing and correcting the proper motion bias of the bright Gaia EDR3 sources
• The Radio Fundamental Catalogue. I. Astrometry
• MultiView High Precision VLBI Astrometry at Low Frequencies
• Bigradient Phase Referencing
• Inverse Multview I: Multi-Calibrator inverse phase referencing for Microarcsecond VLBI Astrometry
• Inverse MultiView II: Microarcsecond Trigonometric Parallaxes for Southern Hemisphere 6.7~GHz Methanol Masers G232.62+00.99 and G323.74$-$00.26
• VLBI Astrometry of Radio Stars to Link Radio and Optical Celestial Reference Frames: Observing Strategies