HIGH-RESOLUTION LINEAR POLARIMETRIC IMAGING FOR THE EVENT HORIZON TELESCOPE

By Andrew A. Chael, Michael D. Johnson, Ramesh Narayan, Sheperd S. Doeleman, J. Wardle, Katherine Louise Bouman. 2016

128 citations recorded by the source corpus.

Images of the linear polarizations of synchrotron radiation around active galactic nuclei (AGNs) highlight their projected magnetic field lines and provide key data for understanding the physics of accretion and outflow from supermassive black holes. The highest-resolution polarimetric images of AGNs are produced with Very Long Baseline Interferometry (VLBI). Because VLBI incompletely samples the Fourier transform of the source image, any image reconstruction that fills in unmeasured spatial frequencies will not be unique and reconstruction algorithms are required. In this paper, we explore some extensions of the Maximum Entropy Method (MEM) to linear polarimetric VLBI imaging. In contrast to previous work, our polarimetric MEM algorithm combines a Stokes I imager that only uses bispectrum measurements that are immune to atmospheric phase corruption, with a joint Stokes Q and U imager that operates on robust polarimetric ratios. We demonstrate the effectiveness of our technique on 7 and 3 mm wavelength quasar observations from the VLBA and simulated 1.3 mm Event Horizon Telescope observations of Sgr A* and M87. Consistent with past studies, we find that polarimetric MEM can produce superior resolution compared to the standard CLEAN algorithm, when imaging smooth and compact source distributions. As an imaging framework, MEM is highly adaptable, allowing a range of constraints on polarization structure. Polarimetric MEM is thus an attractive choice for image reconstruction with the EHT.

Subfields: Astronomy and Astrophysics, Nuclear and High Energy Physics, AI for Physical Sciences

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Astronomy and Astrophysics

In Nuclear and High Energy Physics

In AI for Physical Sciences

N. Wex

Citations in the corpus

Array programming with NumPy (2020, methodology): “the software stack used in the discovery of gravitational waves [6] and the first imaging of a black hole [7].”
Constraints on black-hole charges with the 2017 EHT observations of M87* (2021, methodology): “Software: AIPS [102], ParselTongue [103], GNU Parallel [104], eht-imaging [105], Difmap [106], Numpy [107], Scipy [108], Pandas [109], Astropy [110,111], Jupyter [112], Matplotlib [113], THEMIS [114], DMC [115], polsolve [116], GPCAL [117].”
The current ability to test theories of gravity with black hole shadows (2018, unknown): “a2 = √ 1 + b̂/μ− 1 2 − b̂(2)/(4μ(2)) + b̂/(2μ) (√ 1 + b̂/μ− 1 ) ( 1 + b̂/(2μ) )2 , (15)”
Imaging the Schwarzschild-radius-scale Structure of M87 with the Event Horizon Telescope Using Sparse Modeling (2017, methodology): “We note that the problem is non-convex as other imaging techniques using closure quantities (e.g. Buscher 1994; Thiébaut 2008; Bouman et al. 2015; Chael et al. 2016), and a global solution is generally not guaranteed.”