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Computational Imaging for VLBI Image Reconstruction

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Computational Imaging for VLBI Image Reconstruction Computational Imaging for VLBI Image Reconstruction Katherine L. Bouman1 Michael D. Johnson2 Daniel Zoran1 Vincent L. Fish3 Sheperd S. Doeleman2,3 William T. Freeman1,4 1Massachusetts Institute of Technology, CSAIL 2Harvard, Center for Astrophysics 3MIT Haystack Observatory 4Google Abstract Very long baseline interferometry (VLBI) is a technique for imaging celestial radio emissions by simultaneously ob- serving a source from telescopes distributed across Earth. The challenges in reconstructing images from fine angular resolution VLBI data are immense. The data is extremely sparse and noisy, thus requiring statistical image models such as those designed in the computer vision community. In this paper we present a novel Bayesian approach for VLBI image reconstruction. While other methods often re- (a) Telescope Locations (b) Spatial Frequency Coverage quire careful tuning and parameter selection for different Figure 1. Frequency Coverage: (A) A sample of the telescope locations types of data, our method (CHIRP) produces good results in the EHT. By observing a source over the course of a day, we obtain mea- under different settings such as low SNR or extended emis- surements corresponding to elliptical tracks in the source image’s spatial sion. The success of our method is demonstrated on realis- frequency plane (B). These frequencies, (u,v), are the projected baseline lengths orthogonal to a telescope pair’s light of sight. Points of the same tic synthetic experiments as well as publicly available real color correspond to measurements from the same telescope pair. data. We present this problem in a way that is accessible to members of the community, and provide a dataset website a lack of inverse modeling [36], resulting in sub-optimal im- (vlbiimaging.csail.mit.edu) that facilitates con- ages. Image processing, restoration, sophisticated inference trolled comparisons across algorithms. algorithms, and the study of non-standard cameras are all active areas of computer vision. The computer vision com- 1. Introduction munity’s extensive work in these areas are invaluable to the High resolution celestial imaging is essential for success of these reconstruction methods and can help push progress in astronomy and physics. For example, imag- the limits of celestial imaging. [16, 17, 27, 43]. ing the plasma surrounding a black hole’s event horizon at Imaging distant celestial sources with high resolving high resolution could help answer many important ques- power (i.e. fine angular resolution) requires single-dish tions; most notably, it may substantiate the existence of telescopes with prohibitively large diameters due to the in- black holes [10] as well as verify and test the effects of verse relationship between angular resolution and telescope general relativity [22]. Recently, there has been an inter- diameter [41]. For example, it is predicted that emission national effort to create an Event Horizon Telescope (EHT) surrounding the black hole at the center of the Milky Way capable of imaging a black hole’s event horizon for the first subtends 2.5 10−10 radians [15]. Imaging this emis- time [12, 13]. The angular resolution necessary for this sion with≈ a 10−10×radian resolution at a 1.3 mm wavelength observation is at least an order of magnitude smaller than would require a telescope with a 13000 km diameter. Al- has been previously used to image radio sources [24]. As though a single telescope this large is unrealizable, by si- measurements from the EHT become available, robust al- multaneously collecting data from an array of telescopes gorithms able to reconstruct images in this fine angular res- located around the Earth, it is possible to emulate samples olution regime will be necessary. from a single telescope with a diameter equal to the maxi- Although billions of dollars are spent on astronomical mum distance between telescopes in the array. Using mul- imaging systems to acquire the best images, current recon- tiple telescopes in this manner is referred to as very long struction techniques suffer from unsophisticated priors and baseline interferometry (VLBI) [41]. Refer to Figure 1a. 913 VLBI measurements place a sparse set of constraints on sˆ sˆ the source image’s spatial frequencies. The task of recon- Time Delay: sˆ B sˆ · structing an image from these constraints is highly ill-posed B τg = · and relies heavily on priors to guide optimization. Cur- c rent VLBI image reconstruction techniques have been rea- B sonably successful in imaging large celestial sources with coarse angular resolution. However, as the demand for V2 = A cos(!(t τg)) V = A cos(!t) higher resolving power increases, particularly for the EHT, − X 1 traditional reconstruction algorithms are quickly approach- Mulplicaon A2 ing their limits [28, 38]. [cos(!τg) + cos(2!t !τg)] The difficulty of image reconstruction drastically in- 2 − A2 creases as the angular resolution of a VLBI array im- <> Rc = cos(!τg) Time Average proves. To improve angular resolution (i.e., increase resolv- 2 Figure 2. Simplified Interferometry Diagram: Light is emitted from a ing power), one must either increase the maximum distance distant source and arrives at the telescopes as a plane wave in the direction between two telescopes or decrease the observing wave- sˆ. An additional distance of B · sˆ is necessary for the light to travel to length [41]. Due to the fixed size of Earth, increasing the the farther telescope, introducing a time delay between the received sig- maximum telescope baseline results in a smaller set of pos- nals that varies depending on the source’s location in the sky. The time- averaged correlation of these signals is a sinusoidal function related to the sible telescope sites to choose from. Therefore, algorithms location of the source. This insight is generalized to extended emissions must be designed to perform well with increasingly fewer in the van Cittert-Zernike Thm. and used to relate the time-averaged cor- measurements [28]. Extending VLBI to millimeter and sub- relation to a Fourier component of the emission image in the direction sˆ. mm wavelengths to increase resolution requires overcoming many challenges, all of which make image reconstruction goal is to provide intuition; for additional details we rec- more difficult. For instance, at these short wavelengths, ommend [41]. As with cameras, a single-dish telescope rapidly varying inhomogeneities in the atmosphere intro- is diffraction limited. However, simultaneously collecting duce additional measurement errors [31, 38]. data from an array of telescopes, called an interferometer, In this paper, we leverage ideas from computer vision allows us to overcome the single-dish diffraction limit. to confront these challenges. We present a new algorithm, Figure 2 provides a simplified explanation of a two CHIRP (Continuous High-resolution Image Reconstruction telescope interferometer. Electromagnetic radiation travels using Patch priors), which takes a Bayesian approach and from a point source to the telescopes. However, because novel formulation to solve the ill-posed inverse problem of the telescopes are separated by a distance B, they will not image reconstruction from VLBI measurements. Specifi- receive the signal concurrently. For spatially incoherent ex- cally, the contributions of this algorithm are: tended emissions, the time-averaged correlation of the re- An improved forward model approximation that more ac- ceived signals is equivalent to the projection of this sinu- • curately models spatial-frequency measurements, soidal variation on the emission’s intensity distribution. A simpler problem formulation and optimization strategy This phenomenon is formally described by the van • to model the effects of atmospheric noise on VLBI data. Cittert-Zernike Theorem. The theorem states that, for ideal Furthermore, current interferometry testing datasets are sensors, the time-averaged correlation of the measured sig- small and have noise properties unsuitable for radio wave- nals from two telescopes, i and j, for a single wavelength, lengths [3, 6, 26]. λ, can be approximated as: We introduce a large, realistic VLBI dataset website to −i2π(uℓ+vm) • Γi,j(u,v) e Iλ(ℓ, m)dldm (1) the community (vlbiimaging.csail.mit.edu). ≈ ℓ m This website allows researchers to easily access a large Z Z where I (ℓ, m) is the emission of wavelength λ traveling VLBI dataset, and compare their algorithms to other leading λ from the direction sˆ = (ℓ, m, √1 ℓ2 m2). The dimen- methods. Its automatic evaluation system facilitates unbi- sionless coordinates (u,v) (measured− − in wavelengths) are ased comparisons between algorithms, which are otherwise the projected baseline, B, orthogonal to the line of sight.1 difficult to make and are lacking in the literature. Further- Notice that Eq. 1 is just the Fourier transform of the source more, we hope to make this computational imaging problem emission image, I (ℓ, m). Thus, Γ (u,v) provides a sin- accessible to computer vision researchers, cross-fertilizing λ i,j gle complex Fourier component of I at position (u,v) on the astronomy and computer vision communities. λ the 2D spatial frequency plane. We refer to these mea- 2. A Brief Introduction to VLBI surements, Γi,j, as visibilities. Since the spatial frequency, 1The change in elevation between telescopes can be neglected due to We briefly describe VLBI to provide the necessary back- corrections made in pre-processing. Additionally, for small FOVs wide- ground for building an accurate likelihood model. Our field effects are negligible. 914 (u,v), is proportional to the baseline, B, increasing the dis- the (u,v) frequency plane. After many iterations, the final tance between telescopes increases the resolving power of image of point sources is blurred [18]. Since CLEAN as- the interferometer, allowing it to distinguish finer details. sumes a distribution of point sources, it often struggles with reconstructing images of extended emissions [38]. Earth Rotation Synthesis At a single time, for an N tele- − For mm/sub-mm wavelength VLBI, reconstruction is scope array, we obtain N(N 1) visibility measurements cor- 2 complicated by corruption of the visibility phases.
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