Generalized Redundant Calibration of Radio Inteferferometers. (arXiv:2107.10186v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Adari_P/0/1/0/all/0/1">Prakruth Adari</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Slosar_A/0/1/0/all/0/1">Anže Slosar</a>
Redundant calibration is a technique in radio astronomy that allows
calibration of radio arrays, whose antennas lie on a lattice by exploiting the
fact that redundant baselines should see the same sky signal. Because the
number of measured visibilities scales quadratically with the number of
antennas, but the number of unknowns describing the individual antenna
responses and the available information about the sky scale only linearly with
the array size, the problem is always over-constrained as long as the array is
big and dense enough. This is true even for non-lattice array configurations.
In this work we study a generalized algorithm in which a per-antenna gain is
replaced with a number of gains. We show that it can successfully describe data
from an approximately redundant array on square lattice with pointing and
geometry errors. We discuss the parameterization, limitations and possible
extensions of this algorithm.
Redundant calibration is a technique in radio astronomy that allows
calibration of radio arrays, whose antennas lie on a lattice by exploiting the
fact that redundant baselines should see the same sky signal. Because the
number of measured visibilities scales quadratically with the number of
antennas, but the number of unknowns describing the individual antenna
responses and the available information about the sky scale only linearly with
the array size, the problem is always over-constrained as long as the array is
big and dense enough. This is true even for non-lattice array configurations.
In this work we study a generalized algorithm in which a per-antenna gain is
replaced with a number of gains. We show that it can successfully describe data
from an approximately redundant array on square lattice with pointing and
geometry errors. We discuss the parameterization, limitations and possible
extensions of this algorithm.
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