Platform Deformation Phase Correction for the AMiBA-13 Co-planar Interferometer
Yu-Wei Liao, Kai-Yang Lin, Yau-De Huang, Jiun-Huei Proty Wu, Paul T., P. Ho, Ming-Tang Chen, Chih-Wei Locutus Huang, Patrick M. Koch, Hiroaki, Nishioka, Tai-An Cheng, Szu-Yuan Fu, Guo-Chin Liu, Sandor M. Molnar, Keiichi, Umetsu, Fu-Cheng Wang, Yu-Yen Chang, Chih-Chiang Han

TL;DR
This paper introduces a new method to correct platform deformation in co-planar interferometers, significantly improving image quality and flux recovery in the AMiBA-13 array by modeling and compensating for deformation-induced phase errors.
Contribution
The paper develops a platform deformation correction model based on photogrammetry and optical pointing data, applicable to co-planar and single dish telescopes, enhancing phase accuracy and imaging performance.
Findings
Recovered 50-70% of flux loss after phase correction.
Restored over 90% of source flux with the correction method.
Applicable to other telescopes with deformation issues.
Abstract
We present a new way to solve the platform deformation problem of co-planar interferometers. The platform of a co-planar interferometer can be deformed due to driving forces and gravity. A deformed platform will induce extra components into the geometric delay of each baseline, and change the phases of observed visibilities. The reconstructed images will also be diluted due to the errors of the phases. The platform deformations of The Yuan-Tseh Lee Array for Microwave Background Anisotropy (AMiBA) were modelled based on photogrammetry data with about 20 mount pointing positions. We then used the differential optical pointing error between two optical telescopes to fit the model parameters in the entire horizontal coordinate space. With the platform deformation model, we can predict the errors of the geometric phase delays due to platform deformation with given azimuth and elevation of…
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.
