Performance test of QU-fitting in cosmic magnetism study

Miyashita, Yoshimitsu; Ideguchi, Shinsuke; Nakagawa, Shouta; Akahori, Takuya; Takahashi, Keitaro

doi:10.1093/mnras/sty2862

Cited by 12 publications

(10 citation statements)

References 32 publications

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“…Anderson et al 2015). To our knowledge, there is currently no literature examining the accuracy of QU-fitting when applied to complexity classification specifically, though Miyashita et al (2019) analyse its effectiveness on identifying the structure of twocomponent sources. Brown (2011) suggested Faraday moments as a method to identify complexity, a method later used by Farnes et al (2014) and Anderson et al (2015), but again no literature examines the accuracy.…”

Section: Prior Workmentioning

confidence: 99%

“…Being able to identify which sources are simple lets us produce a reliable rotation measure grid from background sources, and being able to identify which sources might be complex allows us to find sources to follow-up with slower polarisation analysis methods that may require manual oversight, such as QU-fitting (as seen in, e.g. Miyashita et al 2019;O'Sullivan et al 2017). In this paper, we introduce five simple, interpretable features representing polarised spectra, use these features to train machine learning classifiers to identify Faraday complexity, and demonstrate their effectiveness on real and simulated data.…”

Section: Introductionmentioning

confidence: 99%

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Interpretable Faraday complexity classification

Alger

Livingston

McClure-Griffiths

et al. 2021

Publ. Astron. Soc. Aust.

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Faraday complexity describes whether a spectropolarimetric observation has simple or complex magnetic structure. Quickly determining the Faraday complexity of a spectropolarimetric observation is important for processing large, polarised radio surveys. Finding simple sources lets us build rotation measure grids, and finding complex sources lets us follow these sources up with slower analysis techniques or further observations. We introduce five features that can be used to train simple, interpretable machine learning classifiers for estimating Faraday complexity. We train logistic regression and extreme gradient boosted tree classifiers on simulated polarised spectra using our features, analyse their behaviour, and demonstrate our features are effective for both simulated and real data. This is the first application of machine learning methods to real spectropolarimetry data. With 95% accuracy on simulated ASKAP data and 90% accuracy on simulated ATCA data, our method performs comparably to state-of-the-art convolutional neural networks while being simpler and easier to interpret. Logistic regression trained with our features behaves sensibly on real data and its outputs are useful for sorting polarised sources by apparent Faraday complexity.

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Section: Prior Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interpretable Faraday complexity classification

Alger

Livingston

McClure-Griffiths

et al. 2021

Publ. Astron. Soc. Aust.

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“…RMCLEAN is a CLEAN algorithm (Heald, Braun, & Edmonds 2009) that is typically used to deconvolve the RM synthesis signal. Recent studies that use these methods to study the complexity of a Faraday rotated signal include Farnsworth et al (2011), O'Sullivan et al (2012, Ideguchi et al (2014), Kumazaki et al (2014), Sun et al (2015), Pasetto et al (2018), Miyashita et al (2018), Thomson et al (2021). Each method is limited by the range and number of observed wavelengths.…”

Section: Introductionmentioning

confidence: 99%

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

Pratley

Johnston‐Hollitt

Gaensler

2021

Publ. Astron. Soc. Aust.

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Next-generation spectro-polarimetric broadband surveys will probe cosmic magnetic fields in unprecedented detail, using the magneto-optical effect known as Faraday rotation. However, non-parametric methods such as RMCLEAN can introduce non-observable linearly polarised flux into a fitted model at negative wavelengths squared. This leads to Faraday rotation structures that are consistent with the observed data, but would be impossible or difficult to measure. We construct a convex non-parametric QU-fitting algorithm to constrain the flux at negative wavelengths squared to be zero. This allows the algorithm to recover structures that are limited in complexity to the observable region in wavelength squared. We verify this approach on simulated broadband data sets where we show that it has a lower root mean square error and that it can change the scientific conclusions for real observations. We advise using this prior in next-generation broadband surveys that aim to uncover complex Faraday depth structures. We provide a public Python implementation of the algorithm at https://github.com/Luke-Pratley/Faraday-Dreams.

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“…Ndiritu et al (2021) suggested the use of Gaussian process modeling to interpolate gaps in the polarization spectrum to improve the FDF reconstruction. Meanwhile, a popular technique is QU -fitting (Farnsworth et al 2011;O'Sullivan et al 2012;Ozawa et al 2015;Kaczmarek et al 2017;Sakemi et al 2018;Schnitzeler & Lee 2018;Miyashita et al 2019), which assumes that the FDF can be approximated by a single or a combination of simple analytic functions such as Gaussian, top-hat, or delta functions. RM CLEAN (e.g., Heald et al 2009;Anderson et al 2016;Michilli et al 2018) is a matching pursuit algorithm that assumes the FDF to be a collection of pointlike sources.…”

Section: Introductionmentioning

confidence: 99%

Wavelets and sparsity for Faraday tomography

Cooray,

Takeuchi,

Ideguchi

et al. 2021

Preprint

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Faraday tomography through broadband polarimetry can provide crucial information on magnetized astronomical objects, such as quasars, galaxies, or galaxy clusters. However, the limited wavelength coverage of the instruments requires that we solve an ill-posed inverse problem when we want to obtain the Faraday dispersion function (FDF), a tomographic distribution of the magnetoionic media along the line of sight. This paper explores the use of wavelet transforms and the sparsity of the transformed FDFs in the form of wavelet shrinkage (WS) for finding better solutions to the inverse problem. We recently proposed the Constraining and Restoring iterative Algorithm for Faraday Tomography (CRAFT; Cooray et al. 2021), a new flexible algorithm that showed significant improvements over the popular methods such as Rotation Measure Synthesis. In this work, we introduce CRAFT+WS, a new version of CRAFT incorporating the ideas of wavelets and sparsity. CRAFT+WS exhibit significant improvements over the original CRAFT when tested for a complex FDF of realistic Galactic model. Reconstructions of FDFs demonstrate super-resolution in Faraday depth, uncovering previously unseen Faraday complexities in observations. The proposed approach will be necessary for effective cosmic magnetism studies using the Square Kilometre Array and its precursors. The code is made publicly available ‡ .

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Performance test of QU-fitting in cosmic magnetism study

Cited by 12 publications

References 32 publications

Interpretable Faraday complexity classification

Interpretable Faraday complexity classification

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

Wavelets and sparsity for Faraday tomography

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