The Power of Simultaneous Multi-frequency Observations for mm-VLBI: Beyond Frequency Phase Transfer
Guang-Yao Zhao, Juan Carlos Algaba, Sang-Sung Lee, Taehyun Jung,, Richard Dodson, Maria Rioja, Do-Young Byun, Jeffrey Hodgson, Sincheol Kang,, Dae-Won Kim, Jae-Young Kim, Jeong-Sook Kim, Soon-Wook Kim, Motoki Kino,, Atsushi Miyazaki, Jong-Ho Park, Sascha Trippe, Kiyoaki Wajima

TL;DR
This paper introduces FPT-square, a novel calibration technique for mm-VLBI that significantly extends coherence time by calibrating ionospheric effects through simultaneous multi-frequency observations, enabling high-frequency all-sky surveys.
Contribution
The paper presents FPT-square, a new phase transfer method that calibrates ionospheric effects in mm-VLBI, improving coherence time and source imaging capabilities at high frequencies.
Findings
Coherence time extended beyond 8 hours at 3 mm.
Residual phase errors can be canceled with distant calibrators.
Suitable for high-frequency all-sky surveys with weak sources.
Abstract
Atmospheric propagation effects at millimeter wavelengths can significantly alter the phases of radio signals and reduce the coherence time, putting tight constraints on high frequency Very Long Baseline Interferometry (VLBI) observations. In previous works, it has been shown that non-dispersive (e.g. tropospheric) effects can be calibrated with the frequency phase transfer (FPT) technique. The coherence time can thus be significantly extended. Ionospheric effects, which can still be significant, remain however uncalibrated after FPT as well as the instrumental effects. In this work, we implement a further phase transfer between two FPT residuals (i.e. so-called FPT-square) to calibrate the ionospheric effects based on their frequency dependence. We show that after FPT-square, the coherence time at 3 mm can be further extended beyond 8~hours, and the residual phase errors can be…
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.
