Integrating Temporal and Spectral Features of Astronomical Data Using Wavelet Analysis for Source Classification

Temporal and spectral information extracted from a stream of photons received from astronomical sources is the foundation on which we build understanding of various objects and processes in the Universe. Typically astronomers fit a number of models separately to light curves and spectra to extract relevant features. These features are then used to classify, identify, and understand the nature of the sources. However, these feature extraction methods may not be optimally sensitive to unknown properties of light curves and spectra. One can use the raw light curves and spectra as features to train classifiers, but this typically increases the dimensionality of the problem, often by several orders of magnitude. We overcome this problem by integrating light curves and spectra to create an abstract image and using wavelet analysis to extract important features from the image. Such features incorporate both temporal and spectral properties of the astronomical data. Classification is then performed on those abstract features. In order to demonstrate this technique, we have used gamma-ray burst (GRB) data from the NASA's Swift mission to classify GRBs into high- and low-redshift groups. Reliable selection of high-redshift GRBs is of considerable interest in astrophysics and cosmology.Comment: Accepted and Published in 2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Imaging: Earth and Beyond (Washington DC, October 13-15, 2015) Conference Proceeding

Machine-z: Rapid Machine Learned Redshift Indicator for Swift Gamma-ray Bursts

Studies of high-redshift gamma-ray bursts (GRBs) provide important information about the early Universe such as the rates of stellar collapsars and mergers, the metallicity content, constraints on the re-ionization period, and probes of the Hubble expansion. Rapid selection of high-z candidates from GRB samples reported in real time by dedicated space missions such as Swift is the key to identifying the most distant bursts before the optical afterglow becomes too dim to warrant a good spectrum. Here we introduce "machine-z", a redshift prediction algorithm and a "high-z" classifier for Swift GRBs based on machine learning. Our method relies exclusively on canonical data commonly available within the first few hours after the GRB trigger. Using a sample of 284 bursts with measured redshifts, we trained a randomized ensemble of decision trees (random forest) to perform both regression and classification. Cross-validated performance studies show that the correlation coefficient between machine-z predictions and the true redshift is nearly 0.6. At the same time our high-z classifier can achieve 80% recall of true high-redshift bursts, while incurring a false positive rate of 20%. With 40% false positive rate the classifier can achieve ~100% recall. The most reliable selection of high-redshift GRBs is obtained by combining predictions from both the high-z classifier and the machine-z regressor.Comment: Accepted to the Monthly Notices of the Royal Astronomical Society Journal (10 pages, 10 figures, and 3 Tables

VAST: An ASKAP Survey for Variables and Slow Transients

The Australian Square Kilometre Array Pathfinder (ASKAP) will give us an unprecedented opportunity to investigate the transient sky at radio wavelengths. In this paper we present VAST, an ASKAP survey for Variables and Slow Transients. VAST will exploit the wide-field survey capabilities of ASKAP to enable the discovery and investigation of variable and transient phenomena from the local to the cosmological, including flare stars, intermittent pulsars, X-ray binaries, magnetars, extreme scattering events, interstellar scintillation, radio supernovae and orphan afterglows of gamma ray bursts. In addition, it will allow us to probe unexplored regions of parameter space where new classes of transient sources may be detected. In this paper we review the known radio transient and variable populations and the current results from blind radio surveys. We outline a comprehensive program based on a multi-tiered survey strategy to characterise the radio transient sky through detection and monitoring of transient and variable sources on the ASKAP imaging timescales of five seconds and greater. We also present an analysis of the expected source populations that we will be able to detect with VAST.Comment: 29 pages, 8 figures. Submitted for publication in Pub. Astron. Soc. Australi

A Dynamic, Modular Intelligent-Agent framework for Astronomical Light Curve Analysis and Classification

Modern time-domain astronomy is capable of collecting a staggeringly large amount of data on millions of objects in real time. This makes it almost impossible for objects to be identified manually. Therefore the production of methods and systems for the automated classification of time-domain astro-nomical objects is of great importance. The Liverpool Telescope has a number of wide-field image gathering instruments mounted upon its structure. These in-struments have been in operation since March 2009 gathering data of multi-degree sized areas of sky around the current field of view of the main telescope. Utilizing a Structured Query Language database established by a pre-processing operation upon the resultant images, which has identified millions of candidate variable stars with multiple time-varying magnitude observations, we applied a method designed to extract time-translation invariant features from the time-series light curves of each object for future input into a classification system. These efforts were met with limited success due to noise and uneven sampling within the time-series data. Additionally, finely surveying these light curves is a processing intensive task. Fortunately, these algorithms are capable of multi-threaded implementations based on available resources. Therefore we propose a new system designed to utilize multiple intelligent agents that distribute the data analysis across multiple machines whilst simultaneously a powerful intelligence service operates to constrain the light curves and eliminate false signals due to noise and local alias periods. This system will be highly scalable, capable of operating on a wide range of hardware whilst maintaining the production of ac-curate features based on the fitting of harmonic models to the light curves within the initial Structural Query Language database

Fractal Analysis and Chaos in Geosciences

The fractal analysis is becoming a very useful tool to process obtained data from chaotic systems in geosciences. It can be used to resolve many ambiguities in this domain. This book contains eight chapters showing the recent applications of the fractal/mutifractal analysis in geosciences. Two chapters are devoted to applications of the fractal analysis in climatology, two of them to data of cosmic and solar geomagnetic data from observatories. Four chapters of the book contain some applications of the (multi-) fractal analysis in exploration geophysics. I believe that the current book is an important source for researchers and students from universities

Discrete Wavelet Methods for Interference Mitigation: An Application To Radio Astronomy

The field of wavelets concerns the analysis and alteration of signals at various resolutions. This is achieved through the use of analysis functions which are referred to as wavelets. A wavelet is a signal defined for some brief period of time that contains oscillatory characteristics. Generally, wavelets are intentionally designed to posses particular qualities relevant to a particular signal processing application. This research project makes use of wavelets to mitigate interference, and documents how wavelets are effective in the suppression of Radio Frequency Interference (RFI) in the context of radio astronomy. This study begins with the design of a library of smooth orthogonal wavelets well suited to interference suppression. This is achieved through the use of a multi-parameter optimization applied to a trigonometric parameterization of wavelet filters used for the implementation of the Discrete Wavelet Transform (DWT). This is followed by the design of a simplified wavelet interference suppression system, from which measures of performance and suitability are considered. It is shown that optimal performance metrics for the suppression system are that of Shannon’s entropy, Root Mean Square Error (RMSE) and normality testing using the Lilliefors test. From the application of these heuristics, the optimal thresholding mechanism was found to be the universal adaptive threshold and entropy based measures were found to be optimal for matching wavelets to interference. This in turn resulted in the implementation of the wavelet suppression system, which consisted of a bank of matched filters used to determine which interference source is present in a sampled time domain vector. From this, the astronomy based application was documented and results were obtained. It is shown that the wavelet based interference suppression system outperforms existing flagging techniques. This is achieved by considering measures of the number of sources within a radio-image of the Messier 83 (M83) galaxy and the power of the main source in the image. It is shown that designed system results in an increase of 27% in the number of sources in the recovered radio image and a 1.9% loss of power of the main source

VAST: An ASKAP Survey for Variables and Slow Transients

The Australian Square Kilometre Array Pathfinder (ASKAP) will give us an unprecedented opportunity to investigate the transient sky at radio wavelengths. In this paper we present VAST, an ASKAP survey for Variables and Slow Transients. VAST will exploit the wide-field survey capabilities of ASKAP to enable the discovery and investigation of variable and transient phenomena from the local to the cosmological, including flare stars, intermittent pulsars, X-ray binaries, magnetars, extreme scattering events, interstellar scintillation, radio supernovae, and orphan afterglows of gamma-ray bursts. In addition, it will allow us to probe unexplored regions of parameter space where new classes of transient sources may be detected. In this paper we review the known radio transient and variable populations and the current results from blind radio surveys. We outline a comprehensive program based on a multi-tiered survey strategy to characterise the radio transient sky through detection and monitoring of transient and variable sources on the ASKAP imaging timescales of 5 s and greater. We also present an analysis of the expected source populations that we will be able to detect with VAST

Background-Source separation in astronomical images with Bayesian Probability Theory

In this work a new method for the detection of faint, both point-like and extended, astronomical objects based on the integrated treatment of source and background signals is described. This technique is applied to public data obtained by imaging methods of high-energy observational astronomy in the X-ray spectral regime. These data are usually employed to address current astrophysical problems, e.g. in the fields of stellar and galaxy evolution and the large-scale structure of the universe. The typical problems encountered during the analysis of these data are: spatially varying cosmic background, large variety of source morphologies and intensities, data incompleteness, steep gradients in the data, and few photon counts per pixel. These problems are addressed with the developed technique. Previous methods extensively employed for the analysis of these data are, e.g., the sliding window and the wavelet based techniques. Both methods are known to suffer from: describing large variations in the background, detection of faint and extended sources and sources with complex morphologies. Large systematic errors in object photometry and loss of faint sources may occur with these techniques. The developed algorithm is based on Bayesian probability theory, which is a consistent probabilistic tool to solve an inverse problem for a given state of information. The information is given by a parameterized model for the background and prior information about source intensity distributions quantified by probability distributions. For the background estimation, the image data are not censored. The background rate is described by a two-dimensional thin-plate spline function. The background model is given by the product of the background rate and the exposure time which accounts for the variations of the integration time. Therefore, the background as well as effects like vignetting, variations of detector quantum efficiency and strong gradients in the exposure time are being handled properly which results in improved detections with respect to previous methods. Source probabilities are provided for individual pixels as well as for correlations of neighboring pixels in a multi-resolution analysis. Consequently, the technique is able of detecting point-like and extended sources and their complex morphologies. Furthermore, images of different spectral bands can be combined probabilistically to further increase the resolution in crowded regions. The developed method characterizes all detected sources in terms of position, number of source counts, and shape including uncertainties. The comparison with previous techniques shows that the developed method allows for an improved determination of background and source parameters. The method is applied to data obtained by the ROSAT and Chandra X-ray observatories whereas particularly the detection of faint and extended sources is improved with respect to previous analyses. This lead to the discovery of new galaxy clusters and quasars in the X-ray band which are confirmed in the optical regime using additional observational data. The new technique developed in this work is particularly suited to the identification of objects featuring extended emission like clusters of galaxies

Second Annual Conference on Astronomical Data Analysis Software and Systems. Abstracts

Abstracts from the conference are presented. The topics covered include the following: next generation software systems and languages; databases, catalogs, and archives; user interfaces/visualization; real-time data acquisition/scheduling; and IRAF/STSDAS/PROS status reports