VLBI for Gravity Probe B. IV. A New Astrometric Analysis Technique and a Comparison with Results from Other Techniques
D. E. Lebach, N. Bartel, M. F. Bietenholz, R. M. Campbell, D. Gordon,, J. I. Lederman, J.-F. Lestrade, R. R. Ransom, M. I. Ratner, and I. I. Shapiro

TL;DR
This paper introduces a novel 'merged' VLBI astrometric analysis technique that combines advantages of existing methods, improving accuracy and applicability, especially for weak sources, and demonstrates its effectiveness through comparison with traditional approaches.
Contribution
The paper presents a new merged analysis method for VLBI astrometry that enhances model correction and source measurement capabilities, outperforming traditional techniques in accuracy and weak-source analysis.
Findings
The merged technique yields superior astrometric results compared to phase-referenced maps.
It enables astrometric measurements of sources too weak for parametric model fits.
Application to IM Pegasi supports the Gravity Probe B mission.
Abstract
When VLBI observations are used to determine the position or motion of a radio source relative to reference sources nearby on the sky, the astrometric information is usually obtained via: (i) phase-referenced maps; or (ii) parametric model fits to measured fringe phases or multiband delays. In this paper we describe a "merged" analysis technique which combines some of the most important advantages of these other two approaches. In particular, our merged technique combines the superior model-correction capabilities of parametric model fits with the ability of phase-referenced maps to yield astrometric measurements of sources that are too weak to be used in parametric model fits. We compare the results from this merged technique with the results from phase-referenced maps and from parametric model fits in the analysis of astrometric VLBI observations of the radio-bright star IM Pegasi (HR…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
