Real Interferometric Data Research Articles

Dependence of the reconstructed core-collapse supernova gravitational wave high-frequency feature on the nuclear equation of state in real interferometric data

Dependence of the reconstructed core-collapse supernova gravitational wave high-frequency feature on the nuclear equation of state in real interferometric data

An Absolute Phase Estimation Method for Interferometry Imagery

Accurate interpretation of interferometric image requires an extremely challenging task based on actual phase reconstruction for incomplete noise observation. In spite of the establishment of comprehensive solutions, until now, a guaranteed means of solution method is yet to exist. The initially observed interferometric image is formed by 2π-periodic phase image that wrapped within (-π, π]. Such inverse problem is further corrupted by noise distortion and leads to the degradation of interferometric image. In order to overcome this, an effective algorithm that enables noise suppression and absolute phase reconstruction of interferometric phase image is proposed. The proposed method incorporates an improved order statistical filter that is able to adjust or vary on its filtering rate by adapting to phase noise level of relevant interferometric image. Performance of proposed method is evaluated and compared with other existing phase estimation algorithms. The comparison is based on a series of computer simulated and real interferometric data images. The experiment results illustrate the effectiveness and competency of the proposed method.

An Absolute Phase Estimation Method for Interferometry Imagery

Accurate interpretation of interferometric image requires an extremely challenging task based on actual phase reconstruction for incomplete noise observation. In spite of the establishment of comprehensive solutions, until now, a guaranteed means of solution method is yet to exist. The initially observed interferometric image is formed by 2π-periodic phase image that wrapped within (-π, π]. Such inverse problem is further corrupted by noise distortion and leads to the degradation of interferometric image. In order to overcome this, an effective algorithm that enables noise suppression and absolute phase reconstruction of interferometric phase image is proposed. The proposed method incorporates an improved order statistical filter that is able to adjust or vary on its filtering rate by adapting to phase noise level of relevant interferometric image. Performance of proposed method is evaluated and compared with other existing phase estimation algorithms. The comparison is based on a series of computer simulated and real interferometric data images. The experiment results illustrate the effectiveness and competency of the proposed method.

Enhanced Interferometric Phase Noise Filtering of the Refined InSAR Filter

The effectiveness of refined interferometric synthetic aperture radar (InSAR) filter (refined filter) was validated using both simulated and real interferometric data. However, the threshold as a key parameter in the refined filter is determined by repeated attempts, and the deterministic method has some disadvantages, such as strong subjectivity, long time consuming, and bad adaptation. This letter makes full use of the robustness of inverse distance weighting (IDW) to simplify the refined filter. In the proposed filter, using an $11 \times 11$ window and an improved IDW, the computational burden of $| {\langle {e^{j\psi _{z}}} \rangle } |$ for 16 windows per pixel is avoided, and the need for setting the threshold is eliminated. From the beginning, the initial window angle is determined using four preprocessing windows. The filtering direction of the center pixel is affected by the initial window angle of each pixel, the degree of homogeneity of each pixel, and the distance of the pixel from the center pixel in the $11\times 11$ window. The improved weighting method is then used to further refine the window angle of the center pixel. Numerical experiments by simulated and real InSAR data were used to validate the proposed approach. By comparison, the proposed filter greatly improves the efficiency of the refined filter and exhibits the superiority in filtering performance compared to commonly used filters, particularly for regions of low coherence, high-coherence-gradient, and high-phase gradient.

Using machine learning to study the kinematics of cold gas in galaxies

Abstract Next generation interferometers, such as the Square Kilometre Array, are set to obtain vast quantities of information about the kinematics of cold gas in galaxies. Given the volume of data produced by such facilities astronomers will need fast, reliable, tools to informatively filter and classify incoming data in real time. In this paper, we use machine learning techniques with a hydrodynamical simulation training set to predict the kinematic behaviour of cold gas in galaxies and test these models on both simulated and real interferometric data. Using the power of a convolutional autoencoder we embed kinematic features, unattainable by the human eye or standard tools, into a three-dimensional space and discriminate between disturbed and regularly rotating cold gas structures. Our simple binary classifier predicts the circularity of noiseless, simulated, galaxies with a recall of $85\%$ and performs as expected on observational CO and HI velocity maps, with a heuristic accuracy of $95\%$. The model output exhibits predictable behaviour when varying the level of noise added to the input data and we are able to explain the roles of all dimensions of our mapped space. Our models also allow fast predictions of input galaxies’ position angles with a 1σ uncertainty range of ±17○ to ±23○ (for galaxies with inclinations of 82.5○ to 32.5○, respectively), which may be useful for initial parameterisation in kinematic modelling samplers. Machine learning models, such as the one outlined in this paper, may be adapted for SKA science usage in the near future.

Interferometric SAR Phase Denoising Using Proximity-Based K-SVD Technique.

This paper addresses the problem of interferometric noise reduction in Synthetic Aperture Radar (SAR) interferometry based on sparse and redundant representations over a trained dictionary. The idea is to use a Proximity-based K-SVD (ProK-SVD) algorithm on interferometric data for obtaining a suitable dictionary, in order to extract the phase image content effectively. We implemented this strategy on both simulated as well as real interferometric data for the validation of our approach. For synthetic data, three different training dictionaries have been compared, namely, a dictionary extracted from the data, a dictionary obtained by a uniform random distribution in , and a dictionary built from discrete cosine transform. Further, a similar strategy plan has been applied to real interferograms. We used interferometric data of various SAR sensors, including low resolution C-band ERS/ENVISAT, medium L-band ALOS, and high resolution X-band COSMO-SkyMed, all over an area of Mt. Etna, Italy. Both on simulated and real interferometric phase images, the proposed approach shows significant noise reduction within the fringe pattern, without any considerable loss of useful information.

Finding the Origin of Noise Transients in LIGO Data with Machine Learning

Quality improvement of interferometric data collected by gravitational-wave detectors such as Advanced LIGO and Virgo is mission critical for the success of gravitational-wave astrophysics. Gravitational-wave detectors are sensitive to a variety of disturbances of non-astrophysical origin with characteristic frequencies in the instrument band of sensitivity. Removing non-astrophysical artifacts that corrupt the data stream is crucial for increasing the number and statistical significance of gravitational-wave detections and enabling refined astrophysical interpretations of the data. Machine learning has proved to be a powerful tool for analysis of massive quantities of complex data in astronomy and related fields of study. We present two machine learning methods, based on random forest and genetic programming algorithms, that can be used to determine the origin of non-astrophysical transients in the LIGO detectors. We use two classes of transients with known instrumental origin that were identified during the first observing run of Advanced LIGO to show that the algorithms can successfully identify the origin of non-astrophysical transients in real interferometric data and thus assist in the mitigation of instrumental and environmental disturbances in gravitational-wave searches. While the data sets described in this paper are specific to LIGO, and the exact procedures employed were unique to the same, the random forest and genetic programming code bases and means by which they were applied as a dual machine learning approach are completely portable to any number of instruments in which noise is believed to be generated through mechanical couplings, the source of which is not yet discovered.

Reconstruction of the wavefront aberration from real interferometric data using a hybrid evolutionary optimization algorithm with Zernike polynomials

This paper describes the use of a hybrid evolutionary optimization algorithm (HEOA) for computing the wavefront aberration from real interferometric data. By finding the near-optimal solution to an optimization problem, this algorithm calculates the Zernike polynomial expansion coefficients from a Fizeau interferogram, showing the validity for the reconstruction of the wavefront aberration. The proposed HEOA incorporates the advantages of both a multimember evolution strategy and locally weighted linear regression in order to minimize an objective function while avoiding premature convergence to a local minimum. The numerical results dem- onstrate that our HEOA is robust for analyzing real interferograms degraded by noise. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10.1117/1.OE.54.9.094102)

Parallel Branch-Cut Algorithm Based on Simulated Annealing for Large-Scale Phase Unwrapping

Two-dimensional phase unwrapping is a key step in the phase extraction process, an image-processing stage that is common to many different systems. Many varied approaches have been proposed over the past several decades. However, with the growth of image scale, it poses new challenges in terms of computational and memory requirements to phase unwrapping that require a global approach to obtain good results. Owing to only a single process used in most previous algorithm implementations, it becomes more problematic to unwrapping when the required computing resources exceed the capability of one computer. Meanwhile, with the development and application of supercomputer techniques, high-performance computing is emerging as a promising platform for scientific applications. In this paper, a novel hybrid multiprocessing and multithreading algorithm is proposed in order to overcome the problem of unwrapping large data sets. In this algorithm, we improve on Goldstein's branch-cut algorithm using simulated annealing idea to further optimize the set of branch cuts in parallel. For large data sets, the tiling strategy based on the nature of parallel computing guarantees the globality of phase unwrapping and avoids large-scale errors introduced. Using real and simulated interferometric data, we demonstrate that our algorithms are highly competitive with other existing algorithms in speed and accuracy. We also demonstrate that the proposed algorithm can be efficiently parallelized and performed across nodes in a high-performance computing cluster.

Refined Filtering of Interferometric Phase From InSAR Data

Radar interferometry has been widely applied in measuring the terrain height and its changes. The information about surface can be derived from phase interferograms. However, the inherent phase noise reduces the accuracy and reliability of that information. Hence, the minimization of phase noise is essential prior to the retrieval of surface information that is embedded in an interferometric phase. This paper presents a refined filter based on the Lee adaptive complex filter and the improved sigma filter that was originally developed for amplitude image filtering. The basic idea is to adaptively filter the interferometric phase according to the local noise level to minimize the loss of signal for a particular pattern of fringes, including such extreme cases as involving broken fringes, following the removal of undesired pixels. Ultimately, the goals are to preserve the fringe pattern, to reduce phase bias and deviation, to reduce the number of residues, and to minimize the phase error. The preservation of the fringe pattern is particularly of concern in areas of high frequency of fringe and large phase gradient corresponding to steep terrains. The proposed refined filter was validated using both simulated data and real interferometric data. Results demonstrate that the filtering performance is better than that of commonly used filters.

A Self-Initializing PolInSAR Classifier Using Interferometric Phase Differences

This paper describes an unsupervised classifier for polarimetric interferometric synthetic aperture radar (PolInSAR) data. Expectation maximization is used to estimate class parameters that maximize the likelihood of observations in an input data set for a given number of classes. Polarimetric information, in the form of coherency matrices, and interferometric information, in the form of complex coherences, are taken into account. Differences in interferometric phase across different polarization states are explicitly modeled to make the classifier sensitive to the vertical structure of the scene under observation, and the distribution over such phase differences is introduced. The classifier is self-initializing, in that it does not rely on decompositions or thresholds. Classification results obtained for real polarimetric interferometric data are presented and discussed.

Gamma ray bursts and gravitational waves: triggered search strategy in the LIGO science runs

We describe a complete analysis pipeline for detecting and estimating gravitational wave signals in conjunction with observations of astrophysical phenomena with an electromagnetic or particle signature, examples of such phenomena being gamma ray bursts (GRB) and supernovae neutrino bursts. Our data analysis methods explicitly account for non-Gaussian and non-stationary features in real interferometric data. This pipeline is meant for signal searches in conjunction with individual GRB triggers and is being used for the analysis of data from the LIGO science runs (S1 and S2). However, many aspects of the pipeline are quite general and can also be used for other broadband interferometric detectors.

Observational Limit on Gravitational Waves from Binary Neutron Stars in the Galaxy

Using optimal matched filtering, we search 25 hours of data from the LIGO 40-meter prototype laser interferometric gravitational-wave detector for gravitational-wave chirps emitted by coalescing binary systems within our Galaxy. This is the first test of this filtering technique on real interferometric data. An upper limit on the rate R of neutron star binary inspirals in our Galaxy is obtained: with 90% confidence, R< 0.5/hour. Similar experiments with LIGO interferometers will provide constraints on the population of tight binary neutron star systems in the Universe.

A two-dimensional region growing least squares phase unwrapping algorithm for interferometric SAR processing

This paper presents a new two-dimensional (2-D) phase unwrapping (PhU) algorithm based on a least squares (LS) region growing strategy: the wrapped phase image is partitioned in different regions that are sequentially unwrapped via a LS algorithm. Reliable regions are dealt with at the beginning of the procedure, while critical areas are unwrapped in the final steps, thus avoiding error propagation from critical to reliable areas. A conditioned least squares formulation of the phase unwrapping problem is the core of the proposed procedure: this allows the solution to be tied to some known boundary phase values, thus guaranteeing the correct joining of the reconstructed phase in between the different regions and preventing them from being independently unwrapped. The application of the finite element method allows a straightforward implementation of the algorithm in the discrete domain case. Experimental results, carried out on simulated and real interferometric SAR data, show the effectiveness of the proposed algorithm and the improved performances with respect to existing unwrapping procedures.

A novel phase unwrapping method based on network programming

Phase unwrapping is the reconstruction of a function on a grid given its values mod 2/spl pi/. Phase unwrapping is a key problem in all quantitative applications of synthetic aperture radar (SAR) interferometry, but also in other fields. A new phase unwrapping method, which is a different approach from existing techniques, is described and tested. The method starts from the fact that the phase differences of neighboring pixels can be estimated with a potential error that is an integer multiple of 2/spl pi/. This suggests the formulation of the phase unwrapping problem as a global minimization problem with integer variables. Recognizing the network structure underlying the problem makes for an efficient solution. In fact, it is possible to equate the phase unwrapping problem to the problem of finding the minimum cost flow on a network, for the solution of which there exist very efficient techniques. The tests performed on real and simulated interferometric SAR data confirm the validity of the approach.

Two-dimensional regression for interferometric phase extraction

An approach based on two-dimensional iterative nonlinear regression for retrieving phase information from single-frame interferograms was formulated and tested for fluid- and heat-flow measurements. Even though an initial crude phase assignment-i.e., fringe-order numbers at limited data points-is needed, the approach does not require complete phase unwrapping as in conventional techniques. Testing of computer-simulated and real interferometric data shows stable convergence and accurate phase extraction. The method works well under a high noise level, including broken fringes or contaminated regions, with a good noise-cleansing capacity. It provides accuracy at image- or opaque-object boundaries and directly offers spatial-gradient values. A weakness, however, can be intensive computation in the iterative estimation. The method is a good candidate for single-frame interferogram reduction.