Multiple mode gravitational wave detection with a spherical antenna

Apart from being omnidirectional, a solid elastic sphere is a natural multimode and multifrequency device for the detection of gravitational waves (GW). Motion sensing in a spherical GW detector thus requires a multiple set of transducers attached to it at suitable locations. If these are resonant then they exert a significant back action on the larger sphere and, as a consequence, the joint dynamics of the entire system must be properly understood before reliable conclusions can be drawn from data obtained using this system . In this paper, I present and develop an analytical approach to the study of such dynamics, which generalizes the currently existing ones and clarifies their actual range of validity. In addition, the new formalism shows that resonator layouts exist that are alternatives to the highly symmetric Truncated Icosahedron Gravitational Antenna (TIGA) , and that they have potentially interesting properties. I will describe in detail one resonance layout that has mode channels , only requires five resonators per quadrupole-mode sensed and is based on a Pentagonal HexaContrahedron (PHC) polyhedric shape. Also, the perturbative nature of the proposed approach makes it very well adapted to systematically assessing the consequences of realistic mistunings in the device parameters, by robust analytic methods. In order to check the real value of the mathematical model, its predictions have been compared with experimental data from the Louisiana State University (LSU) prototype detector TIGA and agreement between the predictions and data is consistently found to reach a satisfactory precision of four decimal places.     

RFI detection by automated feature extraction and statistical analysis

In this paper we present an interference detection toolbox consisting of a high dynamic range Digital Fast‐Fourier‐Transform spectrometer (DFFT, based on FPGA‐technology) and data analysis software for automated radio frequency interference (RFI) detection. The DFFT spectrometer allows high speed data storage of spectra on time scales of less than a second. The high dynamic range of the device assures constant calibration even during extremely powerful RFI events. The software uses an algorithm which performs a two‐dimensional baseline fit in the time‐frequency domain, searching automatically for RFI signals superposed on the spectral data. We demonstrate, that the software operates successfully on computer‐generated RFI data as well as on real DFFT data recorded at the Effelsberg 100‐m telescope. At 21‐cm wavelength RFI signals can be identified down to the 4σ _rms level. A statistical analysis of all RFI events detected in our observational data revealed that: (1) mean signal strength is comparable to the astronomical line emission of the Milky Way, (2) interferences are polarised, (3) electronic devices in the neighbourhood of the telescope contribute significantly to the RFI radiation. We also show that the radiometer equation is no longer fulfilled in presence of RFI signals. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Gravitational wave background from Population III black hole formation

We study the generation of a stochastic gravitational wave (GW) background produced from a population of core-collapse supernovae, which form black holes in scenarios of structure formation. We obtain, for example, that the formation of a population (Population III) of black holes, in cold dark matter scenarios, could generate a stochastic GW background with a maximum amplitude of   h _BG≃10⁻²⁴  and corresponding closure energy density of  Ω_GW∼10⁻⁷  , in the frequency band   ν _obs≃30–470 Hz  (assuming a maximum efficiency of generation of GWs, namely,   ɛ _GWmax=7×10⁻⁴)  for stars forming at redshifts   z ≃30–10  . We show that it will be possible in the future to detect this isotropic GW background by correlating the signals of a pair of 'advanced' LIGO observatories (LIGO III) at a signal-to-noise ratio of ≃40. We discuss what astrophysical information could be obtained from a positive (or even a negative) detection of such a GW background generated in scenarios such as those studied here. One of them is the possibility of obtaining the initial and final redshifts of the emission period from the observed spectrum of GWs.     

Reliable eigenspectra for new generation surveys

We present a novel technique to overcome the limitations of the applicability of principal component analysis to typical real-life data sets, especially astronomical spectra. Our new approach addresses the issues of outliers, missing information, large number of dimensions and the vast amount of data by combining elements of robust statistics and recursive algorithms that provide improved eigensystem estimates step by step. We develop a generic mechanism for deriving reliable eigenspectra without manual data censoring, while utilizing all the information contained in the observations. We demonstrate the power of the methodology on the attractive collection of the Visible Imaging Multi-Object Spectrograph (VIMOS) Very Large Telescope (VLT) Deep Survey spectra that manifest most of the challenges today, and highlight the improvements over previous workarounds, as well as the scalability of our approach to collections with sizes of the Sloan Digital Sky Survey and beyond.     

Detecting Sunyaev–Zel'dovich clusters with Planck– II. Foreground components and optimized filtering schemes

The Planck mission is the most sensitive all-sky cosmic microwave background (CMB) experiment currently planned. The High-Frequency Instrument (HFI) will be especially suited for observing clusters of galaxies by their thermal Sunyaev–Zel'dovich (SZ) effect. In order to assess Planck 's SZ capabilities in the presence of spurious signals, a simulation is presented that combines maps of the thermal and kinetic SZ effects with a realization of the CMB, in addition to Galactic foregrounds (synchrotron emission, free–free emission, thermal emission from dust, CO-line radiation) as well as the submillimetric emission from celestial bodies of our Solar system. Additionally, observational issues such as the finite angular resolution and spatially non-uniform instrumental noise of Planck 's sky maps are taken into account, yielding a set of all-sky flux maps, the autocorrelation and cross-correlation properties of which are examined in detail. In the second part of the paper, filtering schemes based on scale-adaptive and matched filtering are extended to spherical data sets, that enable the amplification of the weak SZ signal in the presence of all contaminations stated above. The theory of scale-adaptive and matched filtering in the framework of spherical maps is developed, the resulting filter kernel shapes are discussed and their functionality is verified.     

Map-making methods for cosmic microwave background experiments

The map-making step of cosmic microwave background (CMB) data analysis involves linear inversion problems that cannot be performed by a brute-force approach for the large time-lines of today. In this paper we present optimal vector-only map-making methods, which are an iterative COBE method, a Wiener direct filter and a Wiener iterative method. We apply these methods on diverse simulated data, and we show that they produce very well restored maps, by removing nearly completely the correlated noise that appears as intense stripes on the simply pixel-averaged maps. The COBE iterative method can be applied to any signals, assuming the stationarity of the noise in the time-line. The Wiener methods assume both the stationarity of the noise and the sky, which is the case for CMB-only data. We apply the methods to Galactic signals too, and test them on balloon-borne experiment strategies and on a satellite whole-sky survey.     

A method for detecting gravitational waves coincident with gamma-ray bursts

The mechanism for gamma-ray bursters and the detection of gravitational waves (GWs) are two outstanding problems facing modern physics. Many models of gamma-ray bursters predict copious GW emission, so the assumption of an association between GWs and gamma-ray bursts (GRBs) may be testable with existing bar GW detector data. We consider Weber bar data streams in the vicinity of known GRB times and present calculations of the expected signal after co-addition of 1000 GW/GRBs that have been shifted to a common zero time. Our calculations are based on assumptions concerning the GW spectrum and the redshift distribution of GW/GRB sources that are consistent with current GW/GRB models. We discuss further possibilities of GW detection associated with GRBs in light of future bar detector improvements and suggest that co-addition of data from several improved bar detectors may result in detection of GWs (if the GW/GRB assumption is correct) on a time-scale comparable to the LIGO projects.     

Separation of instrumental and astrophysical foregrounds for mapping cosmic microwave background anisotropies

For the most sensitive present and future experiments dedicated to cosmic microwave background (CMB) anisotropy observations, the identification and separation of signals coming from different sources is an important step in the data analysis. This problem of the restitution of signals from the observation of their mixture is classically called 'component separation' in CMB mapping. In this paper, we address the general problem of separating, for millimetre-wave sky-mapping applications, components which include not only astrophysical emissions in two-dimensional maps, but also one-dimensional instrumental effects in the data streams. We show that component separation methods can be adapted to separate simultaneously both astrophysical emissions and components coming from time-dependent foreground signals which originate from the instrument itself. Such general methods can be used for the optimal processing of low-redundancy observations where multi-channel observations are a precious tool to remove systematic effects, as may be the case for the Planck mission.     

Research on Pulsar Candidate Identification Method Based on Deep Residual Neural Network

As the next-generation radio astronomical telescopes continuously improve and develop, the pulsar survey will produce millions of pulsar candidates, which pose considerable challenges for pulsar identification and classification. The rapidly evolving artificial intelligence (AI) techniques are being used for pulsar identification and discovery of new pulsars. Using the pulsar data set observed with the Parkes telescope, namely the High Time Resolution Universe Survey (HTRUS), a 14-layer deep residual network has been designed (called the Residual Network, ResNet) for pulsar candidate classifications. In the HTRUS sample data, the number of non-pulsar candidates (i.e., negative samples) is much larger than that of pulsar candidates (i.e., positive samples). The imbalance between the positive and negative samples is prone to result in model misjudgement. By using the over-sampling technique to enhance the data of positive samples in the training set and adjusting the ratio of positive and negative samples, we have solved this imbalance problem. In the training process, the hyperparameters are adjusted by means of 5-fold cross validation to build the model. The test results indicate that the model can achieve a high precision (98%) and recall (100%), the F1-score can reach 99%, and that the implementation of each sample test needs only 7 ms, it has provided a feasible approach for the future big-data analysis of pulsars.     

Morphological redshift estimates for galaxy clusters in a Sunyaev–Zel'dovich effect survey

We develop a new method to estimate the redshift of galaxy clusters through resolved images of the Sunyaev–Zel'dovich effect (SZE). Our method is based on morphological observables which can be measured by actual and future SZE experiments. We test the method with a set of high-resolution hydrodynamical simulations of galaxy clusters at different redshifts. Our method combines the observables in a principal component analysis. After calibrating the method with an independent redshift estimation for some of the clusters, we show – using a Bayesian approach – how the method can give an estimate of the redshift of the galaxy clusters. Although the error bars given by the morphological redshift estimation are large, it should be useful for future SZE surveys where thousands of clusters are expected to be detected; a first preselection of the high-redshift candidates could be done using our proposed morphological redshift estimator. Although not considered in this work, our method should also be useful to give an estimate of the redshift of clusters in X-ray and optical surveys.     

A fast Bayesian approach to discrete object detection in astronomical data sets – PowellSnakes I

A new fast Bayesian approach is introduced for the detection of discrete objects immersed in a diffuse background. This new method, called PowellSnakes, speeds up traditional Bayesian techniques by (i) replacing the standard form of the likelihood for the parameters characterizing the discrete objects by an alternative exact form that is much quicker to evaluate; (ii) using a simultaneous multiple minimization code based on Powell's direction set algorithm to locate rapidly the local maxima in the posterior and (iii) deciding whether each located posterior peak corresponds to a real object by performing a Bayesian model selection using an approximate evidence value based on a local Gaussian approximation to the peak. The construction of this Gaussian approximation also provides the covariance matrix of the uncertainties in the derived parameter values for the object in question. This new approach provides a speed up in performance by a factor of '100' as compared to existing Bayesian source extraction methods that use Monte Carlo Markov chain to explore the parameter space, such as that presented by Hobson & McLachlan. The method can be implemented in either real or Fourier space. In the case of objects embedded in a homogeneous random field, working in Fourier space provides a further speed up that takes advantage of the fact that the correlation matrix of the background is circulant. We illustrate the capabilities of the method by applying to some simplified toy models. Furthermore, PowellSnakes has the advantage of consistently defining the threshold for acceptance/rejection based on priors which cannot be said of the frequentist methods. We present here the first implementation of this technique (version I). Further improvements to this implementation are currently under investigation and will be published shortly. The application of the method to realistic simulated Planck observations will be presented in a forthcoming publication.     

基于分布式执行框架的低频射电干涉阵列成像管线优化

平方公里阵列(Square Kilometre Array,SKA)项目是建设全球最大射电望远镜的国际合作项目,其灵敏度和测量速度将比当前所有的射电望远镜都要高出一个数量级.连续谱巡天是SKA的主要观测模式之一,基于连续谱成像建立巡天区域的标准星图,将能为后续天文科学研究奠定重要基础.银河系与河外星系全天默奇森宽场阵列拓展巡天(GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended,GLEAM-X)是2018—2020年利用SKA先导望远镜默奇森宽场阵列(Murchison Wide-field Array,MWA)二期拓展阵列开展的新的射电连续谱巡天项目,观测期间积累了大量的低频巡天观测数据.海量观测数据的自动化、大批量处理是SKA望远镜项目所面临的的最大挑战和难题之一,基于分布式执行框架的成像管线优化经验将有助于解决海量数据处理问题.详细介绍了GLEAM-X成像管线并对其进行整合和改进,在中国SKA区域中心原型机(China SKA Regional Centre Prototype,...     

基于深层残差网络的脉冲星候选体分类方法研究

随着下一代射电天文望远镜的不断改进和发展,脉冲星巡天观测将发现数百万个脉冲星候选体,这给脉冲星的识别和新脉冲星的发现带来了巨大挑战,迅速发展的人工智能技术可用于脉冲星识别.使用Parkes望远镜的脉冲星数据集(The High Time Resolution Universe Survey,HTRUS),设计了一个14层深的残差网络(Residual Network,ResNet)进行脉冲星候选体分类.在HTRUS数据样本中,存在非脉冲星候选体(负样本)的数目远远大于脉冲星候选体(正样本)数目的样本非均衡问题,容易产生模型误判.通过使用过采样技术对训练集中的正样本进行数据增强,并调整正负样本的比例,解决了正负样本非均衡问题.训练过程中,使用5折交叉验证来调节超参数,最终构建出模型.测试结果表明,该模型能够取得较高的精确度(Precision)和召回率(Recall),分别为98%和100%,F1分数(F1-score)能够达到99%,每个样本检测完成只需要7 ms,为未来脉冲星大数据分析提供了一个可行的办法.     

Forecast daily indices of solar activity, F10.7, using support vector regression method

The 10.7cm solar radio flux (F10.7), the value of the solar radio emission flux density at a wavelength of 10.7cm, is a useful index of solar activity as a proxy for solar extreme ultraviolet radiation. It is meaningful and important to predict F10.7 values accurately for both long-term (months-years) and short-term (days) forecasting, which are often used as inputs in space weather models. This study applies a novel neural network technique, support vector regression (SVR), to forecasting daily values of F10.7. The aim of this study is to examine the feasibility of SVR in short-term F10.7 forecasting. The approach, based on SVR, reduces the dimension of feature space in the training process by using a kernel-based learning algorithm. Thus, the complexity of the calculation becomes lower and a small amount of training data will be sufficient. The time series of F10.7 from 2002 to 2006 are employed as the data sets. The performance of the approach is estimated by calculating the norm mean square error and mean absolute percentage error. It is shown that our approach can perform well by using fewer training data points than the traditional neural network.     

Semi-automated extraction of digital objective prism spectra

We describe a method for the extraction of spectra from high-dispersion objective prism plates. Our method is a catalogue-driven plate solution approach, making use of the right ascension and declination coordinates for the target objects. In contrast to existing methods of photographic plate reduction, we digitize the entire plate and extract spectra off-line. This approach has the advantages that it can be applied to CCD objective prism images and spectra can be re-extracted (or additional spectra extracted) without having to re-scan the plate. After a brief initial interactive period, the subsequent reduction procedure is completely automatic, resulting in fully reduced, wavelength-justified spectra. We also discuss a method of removing stellar continua using a combination of non-linear filtering algorithms.   The method described is used to extract over 12 000 spectra from a set of 92 objective prism plates. These spectra are used in an associated project to develop automated spectral classifiers based on neural networks.     

Detrending time series for astronomical variability surveys

We present a detrending algorithm for the removal of trends in time series. Trends in time series could be caused by various systematic and random noise sources such as cloud passages, changes of airmass, telescope vibration, CCD noise or defects of photometry. Those trends undermine the intrinsic signals of stars and should be removed. We determine the trends from subsets of stars that are highly correlated among themselves. These subsets are selected based on a hierarchical tree clustering algorithm. A bottom-up merging algorithm based on the departure from normal distribution in the correlation is developed to identify subsets, which we call clusters. After identification of clusters, we determine a trend per cluster by weighted sum of normalized light curves. We then use quadratic programming to detrend all individual light curves based on these determined trends. Experimental results with synthetic light curves containing artificial trends and events are presented. Results from other detrending methods are also compared. The developed algorithm can be applied to time series for trend removal in both narrow and wide field astronomy.     

Primordial non-Gaussianity: local curvature method and statistical significance of constraints on fNL from WMAP data

We test the consistency of estimates of the non-linear coupling constant f _NL using non-Gaussian cosmic microwave background (CMB) maps generated by the method described in the work of Liguori, Matarrese & Moscardini. This procedure to obtain non-Gaussian maps differs significantly from the method used in previous works on the estimation of f _NL. Nevertheless, using spherical wavelets, we find results in very good agreement with Mukherjee & Wang, showing that the two ways of generating primordial non-Gaussian maps give equivalent results. Moreover, we introduce a new method for estimating the non-linear coupling constant from CMB observations by using the local curvature of the temperature fluctuation field. We present both Bayesian credible regions (assuming a flat prior) and proper (frequentist) confidence intervals on f _NL, and discuss the relation between the two approaches. The Bayesian approach tends to yield lower error bars than the frequentist approach, suggesting that a careful analysis of the different interpretations is needed. Using this method, we estimate   f _NL=−10⁺²⁷⁰₋₂₆₀  at the 2σ level (Bayesian) and   f _NL=−10⁺³¹⁰₋₂₇₀  (frequentist). Moreover, we find that the wavelet and the local curvature approaches, which provide similar error bars, yield approximately uncorrelated estimates of f _NL and therefore, as advocated in the work of Cabella et al., the estimates may be combined to reduce the error bars. In this way, we obtain   f _NL=−5 ± 85  and   f _NL=−5 ± 175  at the 1σ and 2σ level respectively using the frequentist approach.     

A preferred-direction statistic for sky maps

Large patterns could exist on the microwave sky as a result of various non-standard possibilities for the large-scale Universe – rotation or shear, non-trivial topology, and single topological defects are specific examples. All-sky (or nearly all-sky) CMB data sets allow us, uniquely, to constrain such exotica, and it is therefore worthwhile to explore a wide range of statistical tests. We describe one such statistic here, which is based on determining gradients and is useful for assessing the level of 'preferred directionality' or 'stripiness' in the map. This method is more general than other techniques for picking out specific patterns on the sky, and it also has the advantage of being easily calculable for the mega-pixel maps which will soon be available. For the purposes of illustration, we apply this statistic to the four-year COBE DMR data. For future CMB maps, we expect this to be a useful statistical test of the large-scale structure of the Universe. In principle, the same statistic could also be applied to sky maps at other wavelengths, to CMB polarization maps, and to catalogues of discrete objects. It may also be useful as a means of checking for residual directionality (e.g. from Galactic or ecliptic signals) in maps.     

An alternative to O−C analysis of variable star periods

For more than a century now astronomers have used the O−C (Observed minus Calculated) method to detect the presence of systematic changes in the periods of variable stars. The method is based on an analysis of residuals from a linear fit to the observed epochs. A rather common error in applications of the method is a failure to make provision for autocorrelation which exists in the data. In this paper we consider a model that accounts for the presence of autocorrelation and develop an alternative to the O−C method of analysis. The proposed method focuses on the frequency domain characteristics of observed periods. Its use is illustrated by application to data from the variable stars X Aurigae and RY Sagittarii.     

大规模脉冲星候选体信号的无监督聚类分析研究

随着500 m口径球面射电望远镜(Five-hundred-meter Aperture Spherical radio Telescope, FAST)等大型射电望远镜的建设和使用,脉冲星巡天数据进入PB时代.为解决如此大量高速采样的标量数据挖掘问题,促进新天文现象的发现,提出一种基于无监督聚类的脉冲星候选体筛选方案.该方案采用基于密度层次、划分方法的混合聚类算法,结合MapReduce/Spark并行计算模型和基于滑动窗口的分组策略,进而提高大量候选体信号筛选的效率.通过在脉冲星数据集HTRU2 (High Time Resolution Universe)上的对比实验,结果表明该算法能取得较高的精确度和召回率,分别是0.946和0.905,并且当并行节点足够时,该算法的时间复杂度相比串行执行明显下降.可见,该方法为脉冲星观测大数据的分析挖掘提供一种可行思路.