WeChat Mini Program
Old Version Features

CS-ROMER: A Novel Compressed Sensing Framework for Faraday Depth Reconstruction

Monthly Notices of the Royal Astronomical Society(2022)

Univ Manchester

Cited 2|Views19
Abstract
ABSTRACT The reconstruction of Faraday depth structure from incomplete spectral polarization radio measurements using the RM synthesis technique is an underconstrained problem requiring additional regularization. In this paper, we present cs-romer: a novel object-oriented compressed sensing framework to reconstruct Faraday depth signals from spectropolarization radio data. Unlike previous compressed sensing applications, this framework is designed to work directly with data that are irregularly sampled in wavelength-squared space and to incorporate multiple forms of compressed sensing regularization. We demonstrate the framework using simulated data for the VLA telescope under a variety of observing conditions, and we introduce a methodology for identifying the optimal basis function for reconstruction of these data, using an approach that can also be applied to data sets from other telescopes and over different frequency ranges. In this work, we show that the delta basis function provides optimal reconstruction for VLA L-band data and we use this basis with observations of the low-mass galaxy cluster Abell 1314 in order to reconstruct the Faraday depth of its constituent cluster galaxies. We use the cs-romer framework to de-rotate the Galactic Faraday depth contribution directly from the wavelength-squared data and to handle the spectral behaviour of different radio sources in a direction-dependent manner. The results of this analysis show that individual galaxies within Abell 1314 deviate from the behaviour expected for a Faraday-thin screen such as the intra-cluster medium and instead suggest that the Faraday rotation exhibited by these galaxies is dominated by their local environments.
More
Translated text
Key words
methods: statistical,techniques: polarimetric,galaxies: clusters: intracluster medium
PDF
Bibtex
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined