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We develop a technique to quantitatively characterize the high-energy electron environment using the Galileo spacecraft solid-state imaging (SSI) instrument and results from 3-D Monte Carlo particle transport simulations in Geant4, validating our findings with the Galileo energetic particle detector (EPD). We postprocess raw SSI images to obtain frames with only the radiation contribution. The camera settings (gain state, filter, and so on) are used to compute the intensity of the observed radiation hits. Simulating the response of the SSI instrument to mono-energetic electron environments, we find that the SSI is capable of detecting ≥10-MeV electrons (>90% of <10-MeV particles are stopped with 95% confidence). Using geometric factors computed for the SSI, we calculate the environment particle flux given a number of pixels with radiation hits. We compare the SSI results to measurements from the Galileo EPD, examining the electron fluxes from the EPD >11-MeV integral flux channel. We find agreement within $3\sigma $ of the EPD data for 43 out of 43 (100%) of the SSI images evaluated. 62% of fluxes are also within $1\sigma $ of the EPD data. This approach can be applied to other sets of imaging data (star trackers) in energetic electron environments, such as those found in geostationary Earth orbit. [ABSTRACT FROM AUTHOR]

We address the problem of automatically coregistering planetary images to a common baseline, introducing a novel generic technique that achieves an unprecedented robustness to different image inputs, thus making batch-mode coregistration achievable without requiring the usual parameter tweaking. We introduce a novel image matching technique, which boosts matching performance even under the most strenuous circumstances, and experimentally demonstrate validation through an extensive experimental multi-instrument setup that includes images from eight high-resolution data sets of the Mars and the Moon. The technique is further tested in a batch-mode processing, in which approximately 1.6% of all high-resolution Martian imagery is coregistered to a common baseline. [ABSTRACT FROM AUTHOR]

In comparison to a single horn-feed system, single dish radio astronomical imaging with a phased-array feed (PAF) has additional noise due to variations in the responses of the various beams used in the creation of the image. This increased noise may obscure weak sources of interest and discredit the ability of a PAF to increase sky survey speed. In order to reduce the beam-to-beam variations, we introduce several new PAF beamforming strategies. A dual constraint beamformer is designed to simultaneously satisfy uniform main beam and noise response constraints, eliminating one aspect of the variation. A rim constraint beamformer that relies on calibrator sources on the rim of the reflector dish to control the spillover pattern variations among beams with eigenvector constraints is introduced and shown to reduce beam-to-beam variations. Finally, by combining the dual constraint and rim constraint beamformers, we are able to construct a beamformer that provides spillover pattern control while meeting specified gain and noise response constraints to reduce the beam-to-beam variations and improve PAF imaging. [ABSTRACT FROM PUBLISHER]

A new method is presented which provides prediction of the spatially variant point spread function for the restoration of astronomical images, distorted by atmospheric turbulence when viewed using ground-based telescopes. Our approach uses reservoir computing to firstly learn the spatio-temporal evolution of aberrations caused by turbulence, and secondly, predicts the space-varying point spread function (PSF) for application of widely-used deconvolution algorithms, resulting in the restoration of astronomical images. In this article, a reservoir-based, recurrent neural network is used to predict modal aberrations that comprise the spatially variant PSF over a wide field-of-view using a time-series ensemble from multiple reference beacons. [ABSTRACT FROM PUBLISHER]

We address the data processing methods needed for imaging with a long baseline optical interferometer. We first describe parametric reconstruction approaches and adopt a general formulation of nonparametric image reconstruction as the solution of a constrained optimization problem. Within this framework, we present two recent reconstruction methods, Mira and Wisard, representative of the two generic approaches for dealing with the missing phase information. Mira is based on an implicit approach and a direct optimization of a Bayesian criterion while Wisard adopts a self-calibration approach and an alternate minimization scheme inspired from radio-astronomy. Both methods can handle various regularization criteria. We review commonly used regularization terms and introduce an original quadratic regularization called ldquosoft support constraintrdquo that favors the object compactness. It yields images of quality comparable to nonquadratic regularizations on the synthetic data we have processed. We then perform image reconstructions, both parametric and nonparametric, on astronomical data from the IOTA interferometer, and discuss the respective roles of parametric and nonparametric approaches for optical interferometric imaging. [ABSTRACT FROM PUBLISHER]

In this paper we introduce a new linear filtering technique, the so-called matrix filters, that maximizes the signal-to-interference ratio of compact sources of unknown intensity embedded in a set of images by taking into account the cross-correlations between the different channels. By construction, the new filtering technique outperforms (or at least equals) the standard matched filter applied on individual images. An immediate application is the detection of extragalactic point sources in cosmic microwave background images obtained at different wavelengths. We test the new technique in two simulated cases: a simple two-channel case with ideal correlated color noise and more realistic simulations of the sky as it will be observed by the low-frequency instrument instrument of the upcoming ESA's Planck mission. In both cases, we observe an improvement with respect to the standard matched filter in terms of signal-to-noise interference, number of detections and number of false alarms. [ABSTRACT FROM PUBLISHER]

The increased sensitivity of future radio telescopes will result in requirements for higher dynamic range within the image as well as better resolution and immunity to interference. In this paper, we propose a new matrix formulation of the imaging equation in the cases of non-co-planar arrays and polarimetric measurements. Then, we improve our parametric imaging techniques in terms of resolution and estimation accuracy. This is done by enhancing both the minimum variance distortionless response (MVDR) parametric imaging, introducing alternative dirty images, and by introducing better power estimates based on least squares, with positive semi-definite constraints. We also discuss the use of robust Capon beamforming and semi-definite programming for solving the self-calibration problem. Additionally, we provide statistical analysis of the bias of the MVDR beamformer for the case of moving array, which serves as a first step in analyzing iterative approaches such as CLEAN and the techniques proposed in this paper. Finally we demonstrate a full deconvolution process based on the parametric imaging techniques and show its improved resolution and sensitivity compared to the CLEAN method. [ABSTRACT FROM PUBLISHER]

We present an original method for reconstructing a 3-D object having two spatial dimensions and one spectral dimension from data provided by the infrared slit spectrograph on board the Spitzer Space Telescope. During acquisition, the light flux is deformed by a complex process comprising four main elements (the telescope aperture, the slit, the diffraction grating, and optical distortion) before it reaches the 2-D sensor. The originality of this work lies in the physical modeling, in integral form, of this process of data formation in continuous variables. The inversion is also approached with continuous variables in a semi-parametric format decomposing the object into a family of Gaussian functions. The estimate is built in a deterministic regularization framework as the minimizer of a quadratic criterion. These specificities give our method the power to over-resolve. Its performance is illustrated using real and simulated data. We also present a study of the resolution showing a 1.5-fold improvement relative to conventional methods. [ABSTRACT FROM PUBLISHER]

Three-dimensional (3-D) maps of the electron density and temperature in the solar atmosphere can be tomographically reconstructed from two-dimensional images that are measured by a variety of ground-based and space-based instruments. The electron density and temperature of the solar corona are fundamental parameters for understanding the physical mechanisms that contribute to space weather, or physical phenomena that can, in extreme cases, have adverse effects on Earth and the near-Earth space environment. New signal processing methods are required to take full advantage of the rich and complex suite of observations that are available from the current generation of Sun-observing spacecraft. In particular, this paper provides signal models and corresponding optimal state estimation methods for reconstructing the dynamic 3-D electron density and temperature structures in the solar corona with focus on the many challenges associated with solar tomography. [ABSTRACT FROM PUBLISHER]

We present methods used to measure the information in an astronomical image, in both a statistical and a deterministic way. We discuss the wavelet transform and noise modeling, and describe how to measure the information and the implications for object detection, filtering, and deconvolution. The perspectives opened up by the range of noise models, catering for a wide range of eventualities in physical science imagery and signals, and the new two-pronged but tightly coupled understanding of the concept of information have given rise to better quality results in applications such as noise filtering, deconvolution, compression, and object (feature) detection. We have illustrated some of these new results in this article. The theoretical foundations of our perspectives have been sketched out. The practical implications, too, are evident from the range of important signal processing problems which we can better address with this armoury of methods. The results described in this work are targeted at information and at relevance. While we have focused on experimental results in astronomical image and signal processing, the possibilities are apparent in many other application domains [ABSTRACT FROM PUBLISHER]

Big data has been an enabler for innovation, reconstruction, and advancement of most sectors of our society, and it's receiving continuous and growing attention from researchers and practitioners in academia, industry, and government. There are, however, still lots of challenges spanning from theoretical foundations, systems, and technology to data policy and standards. This special issue focuses on how big data cuts across systems and applications arenas, and the guest editors' introduction describes the five articles they selected out of 30 submitted to cover a wide spectrum of interesting topics, including feature selection for big data analytics, astronomical image analysis, large-scale network prediction, online URL filtering, and massive transaction clustering. [ABSTRACT FROM AUTHOR]

Columnist Charles Day describes how the Internet boosts the beauty and power of astronomical images compared to seeing those same images on paper. [ABSTRACT FROM PUBLISHER]