Removing non-physical structure in fitted Faraday rotated signals: Non-parametric QU-fitting

Pratley, Luke; Johnston‐Hollitt, M.; Gaensler, B. M.

doi:10.1017/pasa.2021.49

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…Note that early attempts, such as fitting the Q,U spectra directly (Farnsworth et al 2011;O'Sullivan et al 2012) do allow use of the full resolution, but at the expense of requiring prior knowledge of the form of the Faraday spectrum (e.g., number of Faraday thin components). Recently, Pratley et al (2021) have introduced a nonparametric method for Q,U fitting that circumvents this difficulty and can reconstruct more complex spectra. Ndiritu et al (2021) use Gaussian Process Modeling which reduces sidelobe problems from gaps in wavelength coverage and utilizes the full resolution complex spectral information.…”

Section: 𝑚𝑖𝑛mentioning

confidence: 99%

“…It has spawned multiple efforts to deconvolve the direct or "'dirty" Faraday spectrum, to remove the sidelobes arising from the ★ E-mail: larry@umn.edu (LR) incomplete 𝜆 2 coverage (e.g., Heald 2009;Frick et al 2011;Andrecut et al 2012;Ndiritu et al 2021). Also, there is a strong interest in detecting "complexity" in Faraday spectra, i.e., the presence of more than one Faraday component (Brown et al 2019;Alger et al 2021;Cooray et al 2021;Pratley et al 2021). A parallel set of efforts have used parametric methods, e.g.…”

Section: Introductionmentioning

confidence: 99%

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Full resolution deconvolution of complex Faraday spectra

Rudnick

Cotton

2023

Monthly Notices of the Royal Astronomical Society

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Polarized synchrotron emission from multiple Faraday depths can be separated by calculating the complex Fourier transform of the Stokes’ parameters as a function of the wavelength squared, known as Faraday Synthesis. As commonly implemented, the transform introduces an additional term $\lambda _0^2$, which broadens the real and imaginary spectra, but not the amplitude spectrum. We use idealized tests to investigate whether additional information can be recovered with a clean process restoring beam set to the narrower width of the peak in the real “full” resolution spectrum with $\lambda _0^2=0$. We find that the $\lambda _0^2$ choice makes no difference, except for the use of a smaller restoring beam. With this smaller beam, the accuracy and phase stability are unchanged for single Faraday components. However, using the smaller restoring beam for multiple Faraday components we find a) better discrimination of the components, b) significant reductions in blending of structures in tomography images, and c) reduction of spurious features in the Faraday spectra and tomography maps. We also discuss the limited accuracy of information on scales comparable to the width of the amplitude spectrum peak, and note a clean-bias, reducing the recovered amplitudes. We present examples using MeerKAT L-band data. We also revisit the maximum width in Faraday depth to which surveys are sensitive, and introduce the variable Wmax, the width for which the power drops by a factor of 2. We find that most surveys cannot resolve continuous Faraday distributions unless the narrower full restoring beam is used.

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Section: 𝑚𝑖𝑛mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Full resolution deconvolution of complex Faraday spectra

Rudnick

Cotton

2023

Monthly Notices of the Royal Astronomical Society

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“…Such under-constrained deconvolution processes may therefore introduce spurious non-physical structures, whilst also being unable to reconstruct all true physical structures (Macquart et al 2012;Pratley et al 2020). Attempts have been made to address this problem in the literature: Pratley et al (2021) presented a non-parametric QU-fitting method that set 𝜆 2 < 0 values to zero after using the FFT, which can also be seen as a projection where each time Faraday depth space is updated, an FFT is used to set 𝜆 2 < 0 to zero; alternatively, attempts have been made to address such under-constrained problems through the use of a suitable prior, or regularisation, during optimisation in order to compensate for the missing information (e.g. Akiyama et al 2017;Cooray et al 2020;Ndiritu et al 2021).…”

Section: Introductionmentioning

confidence: 99%

CS-ROMER: A novel compressed sensing framework for Faraday depth reconstruction

Cárcamo,

Scaife,

Alexander

et al. 2022

Preprint

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The reconstruction of Faraday depth structure from incomplete spectral polarization radio measurements using the RM Synthesis technique is an under-constrained problem requiring additional regularisation. In this paper we present cs-romer: a novel object-oriented compressed sensing framework to reconstruct Faraday depth signals from spectro-polarization 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 regularisation. We demonstrate the framework using simulated data for the JVLA 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 datasets from other telescopes and over different frequency ranges. In this work we show that the delta basis function provides optimal reconstruction for JVLA 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.

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Semi-supervised rotation measure deconvolution and its application to MeerKAT observations of galaxy clusters

Gustafsson,

Brüggen,

Enßlin

2024

A&A

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Faraday rotation contains information about the magnetic field structure along the line of sight and is an important instrument in the study of cosmic magnetism. Traditional Faraday spectrum deconvolution methods such as RMCLEAN face challenges in resolving complex Faraday dispersion functions and handling large datasets. We developed a deep learning deconvolution model to enhance the accuracy and efficiency of extracting Faraday rotation measures from radio astronomical data, specifically targeting data from the MeerKAT Galaxy Cluster Legacy Survey (MGCLS). We used semi-supervised learning, where the model simultaneously recreates the data and minimizes the difference between the output and the true signal of synthetic data. Performance comparisons with RMCLEAN were conducted on simulated as well as real data for the galaxy cluster Abell 3376. Our semi-supervised model is able to recover the Faraday dispersion for extended rotation measure (RM) components, while accounting for bandwidth depolarization, resulting in a higher sensitivity for high-RM signals, given the spectral configuration of MGCLS. Applied to observations of Abell 3376, we find detailed magnetic field structures in the radio relics, and several active galactic nuclei. We also applied our model to MeerKAT data of Abell 85, Abell 168, Abell 194, Abell 3186, and Abell 3667. We have demonstrated the potential of deep learning for improving RM synthesis deconvolution, providing accurate reconstructions at a high computational efficiency. In addition to validating our data against existing polarization maps, we find new and refined features in diffuse sources imaged with MeerKAT.

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Removing non-physical structure in fitted Faraday rotated signals: Non-parametric QU-fitting

Cited by 5 publications

References 33 publications

Full resolution deconvolution of complex Faraday spectra

Full resolution deconvolution of complex Faraday spectra

CS-ROMER: A novel compressed sensing framework for Faraday depth reconstruction

Semi-supervised rotation measure deconvolution and its application to MeerKAT observations of galaxy clusters

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