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Register a new userAn Iterative Reconstruction Algorithm for Faraday Tomography
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Faraday tomography offers crucial information on the magnetized astronomical objects, such as quasars, galaxies, or galaxy clusters, by observing its magnetoionic media. The observed linear polarization spectrum is inverse Fourier transformed to obtain the Faraday dispersion function (FDF), providing us a tomographic distribution of the magnetoionic media along the line of sight. However, this transform gives a poor reconstruction of the FDF because of the instruments limited wavelength coverage. The current Faraday tomography techniques inability to reliably solve the above inverse problem has noticeably plagued cosmic magnetism studies. We propose a new algorithm inspired by the well-studied area of signal restoration, called the Constraining and Restoring iterative Algorithm for Faraday Tomography (CRAFT). This iterative model-independent algorithm is computationally inexpensive and only requires weak physically-motivated assumptions to produce high fidelity FDF reconstructions. We demonstrate an application for a realistic synthetic model FDF of the Milky Way, where CRAFT shows greater potential over other popular model-independent techniques. The dependence of observational frequency coverage on the various techniques reconstruction performance is also demonstrated for a simpler FDF. CRAFT exhibits improvements even over model-dependent techniques (i.e., QU-fitting) by capturing complex multi-scale features of the FDF amplitude and polarization angle variations within a source. The proposed approach will be of utmost importance for future cosmic magnetism studies, especially with broadband polarization data from the Square Kilometre Array and its precursors. We make the CRAFT code publicly available.
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Photometric observations of planetary transits may show localized bumps, called transit anomalies, due to the possible crossing of photospheric starspots. The aim of this work is to analyze the transit anomalies and derive the temperature profile inside the transit belt along the transit direction. We develop the algorithm TOSC, a tomographic inverse-approach tool which, by means of simple algebra, reconstructs the flux distribution along the transit belt. We test TOSC against some simulated scenarios. We find that TOSC provides robust results for light curves with photometric accuracies better than 1~mmag, returning the spot-photosphere temperature contrast with an accuracy better than 100~K. TOSC is also robust against the presence of unocculted spots, provided that the apparent planetary radius given by the fit of the transit light curve is used in place of the true radius. The analysis of real data with TOSC returns results consistent with previous studies.
In this work, we present a novel centroiding method based on Fourier space Phase Fitting(FPF) for Point Spread Function(PSF) reconstruction. We generate two sets of simulations to test our method. The first set is generated by GalSim with elliptical Moffat profile and strong anisotropy which shifts the center of the PSF. The second set of simulation is drawn from CFHT i band stellar imaging data. We find non-negligible anisotropy from CFHT stellar images, which leads to $sim$0.08 scatter in unit of pixels using polynomial fitting method Vakili and Hogg (2016). And we apply FPF method to estimate the centroid in real space, this scatter reduces to $sim$0.04 in SNR=200 CFHT like sample. In low SNR (50 and 100) CFHT like samples, the background noise dominates the shifting of the centroid, therefore the scatter estimated from different methods are similar. We compare polynomial fitting and FPF using GalSim simulation with optical anisotropy. We find that in all SNR$sim$50, 100 and 200) samples, FPF performs better than polynomial fitting by a factor of $sim$3. In general, we suggest that in real observations there are anisotropy which shift the centroid, and FPF method is a better way to accurately locate it.
Magnetic fields play essential roles in various astronomical objects. Radio astronomy has revealed that magnetic fields are ubiquitous in our Universe. However, the real origin and evolution of magnetic fields is poorly proven. In order to advance our knowledge of cosmic magnetism in coming decades, the Square Kilometre Array (SKA) should have supreme sensitivity than ever before, which provides numerous observation points in the cosmic space. Furthermore, the SKA should be designed to facilitate wideband polarimetry so as to allow us to examine sightline structures of magnetic fields by means of depolarization and Faraday Tomography. The SKA will be able to drive cosmic magnetism of the interstellar medium, the Milky Way, galaxies, AGN, galaxy clusters, and potentially the cosmic web which may preserve information of the primeval Universe. The Japan SKA Consortium (SKA-JP) Magnetism Science Working Group (SWG) proposes the project Resolving 4-D Nature of Magnetism with Depolarization and Faraday Tomography, which contains ten scientific use cases.
The Baikal-GVD is a large scale neutrino telescope being constructed in Lake Baikal. The majority of signal detected by the telescope are noise hits, caused primarily by the luminescence of the Baikal water. Separating noise hits from the hits produced by Cherenkov light emitted from the muon track is a challenging part of the muon event reconstruction. We present an algorithm that utilizes a known directional hit causality criterion to contruct a graph of hits and then use a clique-based technique to select the subset of signal hits.The algorithm was tested on realistic detector Monte-Carlo simulation for a wide range of muon energies and has proved to select a pure sample of PMT hits from Cherenkov photons while retaining above 90% of original signal.
Algebraic models for the reconstruction problem in X-ray computed tomography (CT) provide a flexible framework that applies to many measurement geometries. For large-scale problems we need to use iterative solvers, and we need stopping rules for these methods that terminate the iterations when we have computed a satisfactory reconstruction that balances the reconstruction error and the influence of noise from the measurements. Many such stopping rules are developed in the inverse problems communities, but they have not attained much attention in the CT world. The goal of this paper is to describe and illustrate four stopping rules that are relevant for CT reconstructions.
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