LOFAR facet calibration
R. J. van Weeren, W. L. Williams, M. J. Hardcastle, T. W. Shimwell, D., A. Rafferty, J. Sabater, G. Heald, S. S. Sridhar, T. J. Dijkema, G. Brunetti,, M. Br\"uggen, F. Andrade-Santos, G. A. Ogrean, H. J. A. R\"ottgering, W. A., Dawson, W. R. Forman, F. de Gasperin, C. Jones

TL;DR
This paper introduces a new facet calibration method for LOFAR that effectively corrects direction-dependent errors, enabling high-resolution, deep imaging of the low-frequency radio sky with near-thermal noise limits.
Contribution
The paper presents a novel facet calibration scheme that improves high-resolution imaging quality for LOFAR by addressing direction-dependent calibration errors.
Findings
Achieves near-thermal noise limited images at 5 arcsec resolution
Successfully corrects direction-dependent errors across multiple facets
Meets the specifications of the LOFAR Tier-1 northern survey
Abstract
LOFAR, the Low-Frequency Array, is a powerful new radio telescope operating between 10 and 240 MHz. LOFAR allows detailed sensitive high-resolution studies of the low-frequency radio sky. At the same time LOFAR also provides excellent short baseline coverage to map diffuse extended emission. However, producing high-quality deep images is challenging due to the presence of direction dependent calibration errors, caused by imperfect knowledge of the station beam shapes and the ionosphere. Furthermore, the large data volume and presence of station clock errors present additional difficulties. In this paper we present a new calibration scheme, which we name facet calibration, to obtain deep high-resolution LOFAR High Band Antenna images using the Dutch part of the array. This scheme solves and corrects the direction dependent errors in a number of facets that cover the observed field 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.
