Dynamic and Polarimetric VLBI imaging with a multiscalar approach

Müller, H.; Lobanov, A.

doi:10.1051/0004-6361/202245393

Cited by 7 publications

(5 citation statements)

References 61 publications

(80 reference statements)

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Section: A4 Dog-hitmentioning

confidence: 99%

“…The DoG-HiT package (Müller & Lobanov 2022, 2023b) is a wavelet-based imaging algorithm that uses compressive sensing. DoG-HiT fits the χ 2 data terms while assuming that the image structure is sparsely represented by a small number of wavelets.…”

Section: Dog-hitmentioning

confidence: 99%

“…In the first step we utilize the DoG-HiT algorithm (Müller & Lobanov 2022) to approximate a static total-intensity image and derive the multiresolution support (the set of statistically significant wavelet coefficients). In the second step, we utilize this prior information for the multiresolution support imaging strategy described in Müller & Lobanov (2023b) to add linear polarimetry and solve for the dynamics.…”

Section: A4 Dog-hitmentioning

confidence: 99%

“…DoG-HiT fits the χ 2 data terms while assuming that the image structure is sparsely represented by a small number of wavelets. For the polarimetric and dynamic analysis we follow the description presented in Müller & Lobanov (2023b). Similar to the procedure for eht-imaging outlined in Section 5.3, we use the band-averaged, selfcalibrated, and leakage-corrected data set as a starting point.…”

Section: Dog-hitmentioning

confidence: 99%

“…During the fitting of the static DoG-HiT (Stokes  ) model to the observed visibilities, wavelets that are sensitive primarily to spatial scales associated with gaps in the (u,v) coverage have their coefficients suppressed. This prior information is used for the reconstruction of polarimetric and time-variable data sets by a constrained minimization procedure, i.e., we fit the full Stokes polarimetric visibilities independently for every frame but only vary the coefficients in the multiresolution support (Müller & Lobanov 2023b), by minimizing ( ) ˜t 2 c  and ( ) t 2 c  for every snapshot. For the Stokes  static analysis of the time-variable source Sgr A * , we use the fiducial average image from the totalintensity analysis (Paper III) as an initial guess, self-calibrate the data set to this model, add systematic noise at a level of 2% at every baseline, and calculate the multiresolution support with the main imaging round of DoG-HiT by forward-backward splitting (Müller & Lobanov 2022).…”

Section: A4 Dog-hitmentioning

confidence: 99%

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First Sagittarius A* Event Horizon Telescope Results. VII. Polarization of the Ring

The Event Horizon Telescope Collaboration,

Akiyama,

Alberdi

et al. 2024

ApJL

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The Event Horizon Telescope observed the horizon-scale synchrotron emission region around the Galactic center supermassive black hole, Sagittarius A* (Sgr A*), in 2017. These observations revealed a bright, thick ring morphology with a diameter of 51.8 ± 2.3 μas and modest azimuthal brightness asymmetry, consistent with the expected appearance of a black hole with mass M ≈ 4 × 106 M ⊙. From these observations, we present the first resolved linear and circular polarimetric images of Sgr A*. The linear polarization images demonstrate that the emission ring is highly polarized, exhibiting a prominent spiral electric vector polarization angle pattern with a peak fractional polarization of ∼40% in the western portion of the ring. The circular polarization images feature a modestly (∼5%–10%) polarized dipole structure along the emission ring, with negative circular polarization in the western region and positive circular polarization in the eastern region, although our methods exhibit stronger disagreement than for linear polarization. We analyze the data using multiple independent imaging and modeling methods, each of which is validated using a standardized suite of synthetic data sets. While the detailed spatial distribution of the linear polarization along the ring remains uncertain owing to the intrinsic variability of the source, the spiraling polarization structure is robust to methodological choices. The degree and orientation of the linear polarization provide stringent constraints for the black hole and its surrounding magnetic fields, which we discuss in an accompanying publication.

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Section: A4 Dog-hitmentioning

confidence: 99%

Section: Dog-hitmentioning

confidence: 99%

Section: A4 Dog-hitmentioning

confidence: 99%

Section: Dog-hitmentioning

confidence: 99%

Section: A4 Dog-hitmentioning

confidence: 99%

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First Sagittarius A* Event Horizon Telescope Results. VII. Polarization of the Ring

The Event Horizon Telescope Collaboration,

Akiyama,

Alberdi

et al. 2024

ApJL

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Using multiobjective optimization to reconstruct interferometric data. Part I

Müller¹,

Mus²,

Lobanov³

2023

A&A

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Context. Imaging in radioastronomy is an ill-posed inverse problem. However, with increasing sensitivity and capabilities of telescopes, several strategies have been developed in order to solve this challenging problem. In particular, novel algorithms have recently been proposed using (constrained) nonlinear optimization and Bayesian inference. Aims. The Event Horizon Telescope (EHT) Collaboration convincingly investigated the fidelity of their image reconstructions with large surveys, solving the image reconstruction problem with different optimization parameters. This strategy faces a limitation for the existing methods when imaging active galactic nuclei: Large and expensive surveys solving the problem with different optimization parameters are time-consuming. We present a novel nonconvex, multiobjective optimization modeling approach that gives a different type of claim and may provide a pathway to overcome this limitation. Methods. To this end, we use a multiobjective version of the genetic algorithm (GA): the Multiobjective Evolutionary Algorithm Based on Decomposition, or MOEA/D. The GA strategies explore the objective function by evolutionary operations to find the different local minima and to avoid becoming trapped in saddle points. Results. First, we tested our algorithm (MOEA/D) using synthetic data based on the 2017 EHT array and a possible EHT plus next-generation EHT configuration. We successfully recover a fully evolved Pareto front of nondominated solutions for these examples. The Pareto front divides into clusters of image morphologies representing the full set of locally optimal solutions. We discuss approaches to find the most natural guess among these solutions and demonstrate its performance on synthetic data. Finally, we apply MOEA/D to observations of the black hole shadow in Messier 87 with the EHT data in 2017. Conclusions. The MOEA/D is very flexible and faster than any other Bayesian method, and it explores more solutions than regularized maximum likelihood methods. We have written two papers to present this new algorithm. In the first, we explain the basic idea behind multiobjective optimization and MOEA/D, and we use MOEA/D to recover static images. In the second paper, we extend the algorithm to allow dynamic and (static and dynamic) polarimetric reconstructions.

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Identifying synergies between VLBI and STIX imaging

Müller,

Massa,

Mus

et al. 2024

A&A

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Reconstructing an image from noisy, sparsely sampled Fourier data is an ill-posed inverse problem that occurs in a variety of subjects within science, including data analysis for Very Long Baseline Interferometry (VLBI) and the Spectrometer/Telescope for Imaging X-rays (STIX) with respect to solar observations. The need for high-resolution, high-fidelity imaging fosters the active development of a range of novel imaging algorithms in a variety of different algorithmic settings. However, despite these ongoing, parallel developments, such synergies remain unexplored. We study, for the first time, the synergies between the data analysis for the STIX instrument and VLBI. In particular, we compare the methodologies that have been developed in both fields and evaluate their potential. In this way, we identify key trends in the performance of several algorithmic ideas and draw recommendations for the future spending of resources in the study and implementation of novel imaging algorithms. To this end, we organized a semi-blind imaging challenge with data sets and source structures that are typical for sparse VLBI, specifically in the context of the Event Horizon Telescope (EHT) as well as STIX observations. We used 17 different algorithms from both communities, from six different imaging frameworks, in the challenge, making this work the largest scale code comparison for STIX and VLBI to date. We identified strong synergies between the two communities, as proven by the success of the imaging methods proposed for STIX in imaging VLBI data sets and vice versa. Novel imaging methods outperform the standard CLEAN algorithm significantly in every test case. Improvements over the performance of CLEAN offer deeper updates to the inverse modeling pipeline necessary or, consequently, the possibility to replace inverse modeling with forward modeling. Entropy-based methods and Bayesian methods perform best on STIX data. The more complex imaging algorithms utilizing multiple regularization terms (recently proposed for VLBI) add little to no additional improvements for STIX. However, they do outperform the other methods on EHT data, which correspond to a larger number of angular scales. This work demonstrates the great synergy between the STIX and VLBI imaging efforts and the great potential for common developments. The comparison identifies key trends on the efficacy of specific algorithmic ideas for the VLBI and the STIX setting that may evolve into a roadmap for future developments.

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Dynamic and Polarimetric VLBI imaging with a multiscalar approach

Cited by 7 publications

References 61 publications

First Sagittarius A* Event Horizon Telescope Results. VII. Polarization of the Ring

First Sagittarius A* Event Horizon Telescope Results. VII. Polarization of the Ring

Using multiobjective optimization to reconstruct interferometric data. Part I

Identifying synergies between VLBI and STIX imaging

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