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The behavior of coronal holes (CHs) as open magnetic field lines and a substrate for the high-speed exit of solar winds is an essential aspect of solar activity. This paper studies the statistical properties of CHs around the maximum activity of cycle 24 (13 May 2010 to 31 October 2017) observed by the Atmospheric Imaging Assembly at 193 Å images. We showed that the size of CHs follows the power-law distributions indicating the scale-free behavior of CHs. The butterfly diagram of CHs demonstrates that the overall number of CHs shows a similar distribution in the two hemispheres. Large CHs with sizes ⩾ 2.57 × 10 5 arcsec2 (top 3% of observed CHs) more commonly appear at latitudes above 30 ∘ closer to the poles. We found that the largest CHs with sizes ⩾ 7.71 × 10 5 arcsec2 (top 0.03% of observed CHs) occur in the northern hemisphere. The appearance and disappearance of large CHs exhibit the asymmetrical behavior between the two hemispheres. Also, it is observed that CHs tend to have a latitudinal drift towards the poles in each hemisphere. A significant correlation between the number of sunspots and the eccentricity of equatorial CHs appeared near the solar disk center revealed that most of the CHs have elongated shapes at the maximum solar activity. In contrast, they have less abnormality during solar minimum. Applying detrended fluctuation and rescaled range analyses to the time series of CHs, we realized that the CHs have a long-term correlation and form a self-organized system. [ABSTRACT FROM AUTHOR]

We study the machine learning method for classifying the basic shape of space debris in both simulated and observed data experiments, where light curves are used as the input features. In the dataset for training and testing, simulated light curves are derived from four types of debris within different shapes and materials. Observed light curves are extracted from Mini-Mega TORTORA (MMT) database which is a publicly accessible source of space object photometric records. The experiments employ the deep convolutional neural network, make comparisons with other machine learning algorithms, and the results show CNN (Convolutional Neural Network) is better. In simulational experiments, both types of cylinder can be distinguished perfectly, and two other types of satellite have around 90% probability to be classified. Rockets and defunct satellites can achieve 99% success rate in binary classification, but in further sub-classes classifications, the rate becomes relatively lower. [ABSTRACT FROM AUTHOR]

Two line element (TLE) released by the North American Aerospace Defense Command (NORAD) is widely used by aerospace workers, and the matched SGP4/SDP4 (Simplified General Perturbation Version 4/Simplified Deep-space Perturbation Version 4) model is used to propagate it. Nevertheless, no corresponding information about its accuracy and covariance is clearly given, thus the application of the TLE data is greatly restricted. In this paper, the determined and predicted orbits are compared to generate the orbit error data, based on the historical TLE data obtained from the Space-Track website and the SGP4/SDP4 model. By dividing different time bins, the fitting coefficients of the variation of orbit prediction error with time are given for each space object, and the characteristics of the error evolution are further discussed for the different types of orbits. The mean analytic model of the orbit prediction error evolution with time is given respectively for the four orbit types of space objects, which provides a valuable reference for extending the application of the TLE data. [ABSTRACT FROM AUTHOR]

A method for the determination of [ α /Fe] from low-resolution stellar spectra is presented. The proposed scheme includes the following three steps: firstly, the spectrum is decomposed by the multi-scale Haar wavelet, and the high-frequency components are removed to suppress the high-frequency noise; then, based on the correlation of the spectral data component with [ α /Fe], the spectral features are selected by the LASSO (Least Absolute Shrinkage and Selection Operator) algorithm; finally, [ α /Fe] is measured by the multiple linear regression method based on the MARCS stellar spectrum library. The effectiveness of the method is verified with the low-resolution stellar spectra of ELODIE, SDSS (Sloan Digital Sky Survey), LAMOST (Large Sky Area Multi-Object Fibre Spectroscopic Telescope), and four star clusters. The systematic deviations and accuracies are as follows: (0.04 dex, 0.064 dex) for the 317 ELODIE spectra; (0.16 dex, 0.065 dex) for the 412 SDSS spectra; (0.05 dex, 0.062 dex) for the 1276 LAMOST spectra (with the signal-noise ratio in the g band (SNRG) greater than 20). The averages of [ α /Fe] obtained for the likely members of the globular star clusters (M13, M15) and open star clusters (NGC2420, M67) are in agreement with the literature values. [ABSTRACT FROM AUTHOR]

We present a method called Significant Cosmic Holes in Universe (SCHU) for identifying cosmic voids and loops of filaments in cosmological datasets and assigning their statistical significance using techniques from topological data analysis. In particular, persistent homology is used to find different dimensional holes. For dark matter halo catalogs and galaxy surveys, the 0-, 1-, and 2-dimensional holes can be identified with connected components (i.e. clusters), loops of filaments, and voids. The procedure overlays dark matter halos/galaxies on a three-dimensional grid, and a distance-to-measure (DTM) function is calculated at each point of the grid. A nested set of simplicial complexes (a filtration) is generated over the lower-level sets of the DTM across increasing threshold values. The filtered simplicial complex can then be used to summarize the birth and death times of the different dimension homology group generators (i.e., the holes). Persistent homology summary diagrams, called persistence diagrams, are produced from the dimension, birth times, and death times of each homology group generator. Using the persistence diagrams and bootstrap sampling, we explain how p -values can be assigned to each homology group generator. The homology group generators on a persistence diagram are not, in general, uniquely located back in the original dataset volume so we propose a method for finding a representation of the homology group generators. This method provides a novel, statistically rigorous approach for locating informative generators in cosmological datasets, which may be useful for providing complementary cosmological constraints on the effects of, for example, the sum of the neutrino masses. The method is tested on a Voronoi foam simulation, and then subsequently applied to a subset of the SDSS galaxy survey and a cosmological simulation. Lastly, we calculate Betti functions for two of the MassiveNuS simulations and discuss implications for using the persistent homology of the density field to help break degeneracy in the cosmological parameters. [ABSTRACT FROM AUTHOR]

The local Cosmic Ray (CR) energy spectrum exhibits a spectral softening at energies around 3 PeV. Sources which are capable of accelerating hadrons to such energies are called hadronic PeVatrons. However, hadronic PeVatrons have not yet been firmly identified within the Galaxy. Several source classes, including Galactic Supernova Remnants (SNRs), have been proposed as PeVatron candidates. The potential to search for hadronic PeVatrons with the Cherenkov Telescope Array (CTA) is assessed. The focus is on the usage of very high energy γ -ray spectral signatures for the identification of PeVatrons. Assuming that SNRs can accelerate CRs up to knee energies, the number of Galactic SNRs which can be identified as PeVatrons with CTA is estimated within a model for the evolution of SNRs. Additionally, the potential of a follow-up observation strategy under moonlight conditions for PeVatron searches is investigated. Statistical methods for the identification of PeVatrons are introduced, and realistic Monte-Carlo simulations of the response of the CTA observatory to the emission spectra from hadronic PeVatrons are performed. Based on simulations of a simplified model for the evolution for SNRs, the detection of a γ -ray signal from in average 9 Galactic PeVatron SNRs is expected to result from the scan of the Galactic plane with CTA after 10 h of exposure. CTA is also shown to have excellent potential to confirm these sources as PeVatrons in deep observations with O (100) hours of exposure per source. [ABSTRACT FROM AUTHOR]

Objective detection of specific patterns in statistical distributions, like groupings or gaps or abrupt transitions between different subsets, is a task with a rich range of applications in astronomy: Milky Way stellar population analysis, investigations of the exoplanets diversity, Solar System minor bodies statistics, extragalactic studies, etc. We adapt the powerful technique of the wavelet transforms to this generalized task, making a strong emphasis on the assessment of the patterns detection significance. Among other things, our method also involves optimal minimum-noise wavelets and minimum-noise reconstruction of the distribution density function. Based on this development, we construct a self-closed algorithmic pipeline aimed to process statistical samples. It is currently applicable to single-dimensional distributions only, but it is flexible enough to undergo further generalizations and development. [ABSTRACT FROM AUTHOR]

Time delay and phase fluctuation are produced when the signals of a spacecraft are transmitted through the ionosphere of the earth, which give rise to a great influence on the measurement precision of VLBI (Very Long Baseline Interferometry). Using the 1-year same-beam VLBI data of 2 satellites (Rstar and Vstar) in the Japanese lunar exploration project SELENE, we obtained a model of the relation between the fluctuation of double differential total electron content in the ionosphere and the angular distance of the two satellites. For the 6 baselines, the root mean square r of fluctuation (in units of TECU) and the angular distance of the two satellites θ (in units of ∘ ) has a relation of r = 0 . 773 θ + 0 . 562, and for the 4 VLBI stations, the relation is r = 0 . 554 θ + 0 . 399 from the baselines inversion. The results can serve as a reference for the derivation of differential phase delay and for the occultation observation and study of planetary ionospheres. [ABSTRACT FROM AUTHOR]

The “Tapered Gridded Estimator” (TGE) is a novel way to directly estimate the angular power spectrum from radio-interferometric visibility data that reduces the computation by efficiently gridding the data, consistently removes the noise bias, and suppresses the foreground contamination to a large extent by tapering the primary beam response through an appropriate convolution in the visibility domain. Here we demonstrate the effectiveness of TGE in recovering the diffuse emission power spectrum through numerical simulations. We present details of the simulation used to generate low frequency visibility data for sky model with extragalactic compact radio sources and diffuse Galactic synchrotron emission. We then use different imaging strategies to identify the most effective option of point source subtraction and to study the underlying diffuse emission. Finally, we apply TGE to the residual data to measure the angular power spectrum, and assess the impact of incomplete point source subtraction in recovering the input power spectrum C ℓ of the synchrotron emission. This estimator is found to successfully recovers the C ℓ of input model from the residual visibility data. These results are relevant for measuring the diffuse emission like the Galactic synchrotron emission. It is also an important step towards characterizing and removing both diffuse and compact foreground emission in order to detect the redshifted 21 cm signal from the Epoch of Reionization. [ABSTRACT FROM AUTHOR]

Atmospheric seeing is one of the most important parameters for evaluating and monitoring an astronomical site. Moreover, being able to predict the seeing in advance can guide observing decisions and significantly improve the efficiency of telescopes. However, it is not always easy to obtain long-term and continuous seeing measurements from a standard instrument such as differential image motion monitor (DIMM), especially for those unattended observatories with challenging environments such as Dome A, Antarctica. In this paper, we present a novel machine learning-based framework for estimating and predicting seeing at a height of 8 m at Dome A, Antarctica, using only the data from a multi-layer automated weather station (AWS). In comparison with DIMM data, our estimate has a root mean square error (RMSE) of 0.18 arcsec, and the RMSE of predictions 20 min in the future is 0.12 arcsec for the seeing range from 0 to 2.2 arcsec. Compared with the persistence, where the forecast is the same as the last data point, our framework reduces the RMSE by 37 per cent. Our method predicts the seeing within a second of computing time, making it suitable for real-time telescope scheduling. [ABSTRACT FROM AUTHOR]

The increase in data volume is challenging the suitability of non-distributed and non-scalable algorithms, despite advancements in hardware. An example of this challenge is clustering. Considering that optimal clustering algorithms scale poorly with increased data volume or are intrinsically non-distributed, accurate clustering of large datasets is increasingly resource-heavy, relying on substantial and expensive compute nodes. This scenario forces users to choose between accuracy and scalability. In this work, we introduce HiErArchical Data Splitting and Stitching (HEADSS), a Python package designed to facilitate clustering at scale. By automating the splitting and stitching, it allows repeatable handling, and removal, of edge effects. We implement HEADSS in conjunction with HDBSCAN, where we achieve orders of magnitude reduction in single node memory requirements for both non-distributed and distributed implementations, with the latter offering similar order of magnitude reductions in total run times while recovering analogous accuracy. Furthermore, our method establishes a hierarchy of features by using a subset of clustering features to split the data. 1 1 Source code and examples are available at https://github.com/D-Crake/HEADSS. [ABSTRACT FROM AUTHOR]

Deep learning is a typical learning method widely studied in the fields of machine learning, pattern recognition, and artificial intelligence. This work investigates the problem of stellar atmospheric parameterization by constructing a deep neural network with five layers, and the node number in each layer of the network is respectively 3821-500-100-50-1. The proposed scheme is verified on both the real spectra measured by the Sloan Digital Sky Survey (SDSS) and the theoretic spectra computed with the Kurucz's New Opacity Distribution Function (NEWODF) model, to make an automatic estimation for three physical parameters: the effective temperature ( T eff ), surface gravitational acceleration (lg g ), and metallic abundance (Fe/H). The results show that the stacked autoencoder deep neural network has a better accuracy for the estimation. On the SDSS spectra, the mean absolute errors (MAEs) are 79.95 for T eff /K, 0.0058 for (lg T eff /K), 0.1706 for lg ( g /(cm·s −2 )), and 0.1294 dex for the [Fe/H], respectively; On the theoretic spectra, the MAEs are 15.34 for T eff /K, 0.0011 for lg ( T eff /K), 0.0214 for lg( g /(cm · s −2 )), and 0.0121 dex for [Fe/H], respectively. [ABSTRACT FROM AUTHOR]

The multi-task learning takes the multiple tasks together to make analysis and calculation, so as to dig out the correlations among them, and therefore to improve the accuracy of the analyzed results. This kind of methods have been widely applied to the machine learning, pattern recognition, computer vision, and other related fields. This paper investigates the application of multi-task learning in estimating the stellar atmospheric parameters, including the surface temperature ( T eff ), surface gravitational acceleration (lg g ), and chemical abundance ([Fe/H]). Firstly, the spectral features of the three stellar atmospheric parameters are extracted by using the multi-task sparse group Lasso algorithm, then the support vector machine is used to estimate the atmospheric physical parameters. The proposed scheme is evaluated on both the Sloan stellar spectra and the theoretical spectra computed from the Kurucz's New Opacity Distribution Function (NEWODF) model. The mean absolute errors (MAEs) on the Sloan spectra are: 0.0064 for lg ( T eff /K), 0.1622 for lg ( g /(cm · s −2 )), and 0.1221 dex for [Fe/H]; the MAEs on the synthetic spectra are 0.0006 for lg ( T eff /K), 0.0098 for lg ( g /(cm · s −2 )), and 0.0082 dex for [Fe/H]. Experimental results show that the proposed scheme has a rather high accuracy for the estimation of stellar atmospheric parameters. [ABSTRACT FROM AUTHOR]

Photospheric bright points (PBPs) are tiny short-lived brightening phenomena that can be seen within dark inter-granular lanes. In this paper, we develop a new method to identify and track a PBP in a 3D space-time cube. Different from the common-adopted strategy of “identifying-before-tracking”, this algorithm is based on the strategy of “identifying-by-tracking”. This method can identify a PBP when its intensity is still rather weak, and can avoid the discontinuous evolution of PBP caused by the algorithm of Laplacian morphological dilation (LMD) to recognize completely the evolutionary process of a PBP. The statistics on a group of G-band data observed by Hinode/SOT (Solar Optical Telescope) in quiet regions indicate that the average lifetime of isolated PBP is as long as 3 minutes, and the longest lifetime is up to 27 minutes, which means that the lifetime of PBPs is longer than that obtained by the previous LMD algorithm. Furthermore, it is also found that the mean intensity contrast of PBPs is 1.02 times of the mean photospheric intensity, which is weaker than that calculated by the LMD algorithm, and that the intensity of PBP exhibits a periodical oscillation of 2∼3 minute during the whole lifetime. [ABSTRACT FROM AUTHOR]

This paper describes the Vartools program, which is an open-source command-line utility, written in C, for analyzing astronomical time-series data, especially light curves. The program provides a general-purpose set of tools for processing light curves including signal identification, filtering, light curve manipulation, time conversions, and modeling and simulating light curves. Some of the routines implemented include the Generalized Lomb–Scargle periodogram, the Box-Least Squares transit search routine, the Analysis of Variance periodogram, the Discrete Fourier Transform including the CLEAN algorithm, the Weighted Wavelet Z-Transform, light curve arithmetic, linear and non-linear optimization of analytic functions including support for Markov Chain Monte Carlo analyses with non-trivial covariances, characterizing and/or simulating time-correlated noise, and the TFA and SYSREM filtering algorithms, among others. A mechanism is also provided for incorporating a user’s own compiled processing routines into the program. Vartools is designed especially for batch processing of light curves, including built-in support for parallel processing, making it useful for large time-domain surveys such as searches for transiting planets. Several examples are provided to illustrate the use of the program. [ABSTRACT FROM AUTHOR]

Filamentary structures (one-dimensional manifolds) are ubiquitous in astronomical data sets. Be it in particle simulations or observations, filaments are always tracers of a perturbation in the equilibrium of the studied system and hold essential information on its history and future evolution. However, the recovery of such structures is often complicated by the presence of a large amount of background and transverse noise in the observation space. While the former is generally considered detrimental to the analysis, the latter can be attributed to measurement errors and it can hold essential information about the structure. To further complicate the scenario, one-dimensional manifolds (filaments) are generally non-linear and their geometry difficult to extract and model. Thus, in order to study hidden manifolds within the dataset, particular care has to be devoted to background noise removal and transverse noise modeling, while still maintaining accuracy in the recovery of their geometrical structure. We propose 1-DREAM: a toolbox composed of five main Machine Learning methodologies whose aim is to facilitate manifold extraction in such cases. Each methodology has been designed to address particular issues when dealing with complicated low-dimensional structures convoluted with noise and it has been extensively tested in previously published works. However, for the first time, in this work all methodologies are presented in detail, joint within a cohesive framework and demonstrated for three particularly interesting astronomical cases: a simulated jellyfish galaxy, a filament extracted from a simulated cosmic web and the stellar stream of Omega-Centauri as observed with the GAIA DR2. Two newly developed visualization techniques are also proposed, that take full advantage of the results obtained with 1-DREAM. This contribution presents the toolbox in all its details and the code is made publicly available to benefit the community. The controlled experiments on a purposefully built data set prove the accuracy of the pipeline in recovering the real underlying structures. [Display omitted] • Toolbox with five semi-automated methods for manifold extraction and modeling. • Background noise removal; • Transverse noise attenuation; • Point-dimensionality estimation; • Manifold's mean curve extraction and corresponding transverse noise modeling. • Two visualization approaches to represent information on extracted manifolds. • Global: Bi-dimensional profile. Information shown along main manifold's directions; • Local: coherent orthonormal coordinate frames. Orthogonal plane shifted along length. • Demonstration of the toolbox to three interesting astronomical cases. • Simulated dwarf galaxy with elongated arms. • Extracted simulated cosmic web filament. • Omega-Centauri stream as observed via the GAIA DR2. [ABSTRACT FROM AUTHOR]

The duration distribution of 947 GRBs observed by Swift /BAT, as well as its subsample of 347 events with measured redshift, allowing to examine the durations in both the observer and rest frames, are examined. Using a maximum log-likelihood method, mixtures of two and three standard Gaussians are fitted to each sample, and the adequate model is chosen based on the value of the difference in the log-likelihoods, Akaike information criterion and Bayesian information criterion. It is found that a two-Gaussian is a better description than a three-Gaussian, and that the presumed intermediate-duration class is unlikely to be present in the Swift duration data. [ABSTRACT FROM AUTHOR]

The cosmic web is a highly complex geometrical pattern, with galaxy clusters at the intersection of filaments and filaments at the intersection of walls. Identifying and describing the filamentary network is not a trivial task due to the overwhelming complexity of the structure, its connectivity and the intrinsic hierarchical nature. To detect and quantify galactic filaments we use the Bisous model, which is a marked point process built to model multi-dimensional patterns. The Bisous filament finder works directly with the galaxy distribution data and the model intrinsically takes into account the connectivity of the filamentary network. The Bisous model generates the visit map (the probability to find a filament at a given point) together with the filament orientation field. Using these two fields, we can extract filament spines from the data. Together with this paper we publish the computer code for the Bisous model that is made available in GitHub. The Bisous filament finder has been successfully used in several cosmological applications and further development of the model will allow to detect the filamentary network also in photometric redshift surveys, using the full redshift posterior. We also want to encourage the astro-statistical community to use the model and to connect it with all other existing methods for filamentary pattern detection and characterisation. [ABSTRACT FROM AUTHOR]

In astronomical applications of machine learning, the distribution of objects used for building a model is often different from the distribution of the objects the model is later applied to. This is known as sample selection bias, which is a major challenge for statistical inference as one can no longer assume that the labeled training data are representative. To address this issue, one can re-weight the labeled training patterns to match the distribution of unlabeled data that are available already in the training phase. There are many examples in practice where this strategy yielded good results, but estimating the weights reliably from a finite sample is challenging. We consider an efficient nearest neighbor density ratio estimator that can exploit large samples to increase the accuracy of the weight estimates. To solve the problem of choosing the right neighborhood size, we propose to use cross-validation on a model selection criterion that is unbiased under covariate shift. The resulting algorithm is our method of choice for density ratio estimation when the feature space dimensionality is small and sample sizes are large. The approach is simple and, because of the model selection, robust. We empirically find that it is on a par with established kernel-based methods on relatively small regression benchmark datasets. However, when applied to large-scale photometric redshift estimation, our approach outperforms the state-of-the-art. [ABSTRACT FROM AUTHOR]

The multi-task learning approaches have attracted the increasing attention in the fields of machine learning, computer vision, and artificial intelligence. By utilizing the correlations in tasks, learning multiple related tasks simultaneously is better than learning each task independently. An efficient multi-task Lasso (Least Absolute Shrinkage Selection and Operator) regression algorithm is proposed in this paper to estimate the physical parameters of stellar spectra. It not only can obtain the information about the common features of the different physical parameters, but also can preserve effectively their own peculiar features. Experiments were done based on the ELODIE synthetic spectral data simulated with the stellar atmospheric model, and on the SDSS data released by the American large-scale survey Sloan. The estimation precision of our model is better than those of the methods in the related literature, especially for the estimates of the gravitational acceleration (lg g) and the chemical abundance ([Fe/H]). In the experiments we changed the spectral resolution, and applied the noises with different signal-to-noise ratios (SNRs) to the spectral data, so as to illustrate the stability of the model. The results show that the model is influenced by both the resolution and the noise. But the influence of the noise is larger than that of the resolution. In general, the multi-task Lasso regression algorithm is easy to operate, it has a strong stability, and can also improve the overall prediction accuracy of the model. [ABSTRACT FROM AUTHOR]

Abstract: This is a parallelized algorithm performing a decomposition of a noisy time series into a number of sinusoidal components. The algorithm analyses all suspicious periodicities that can be revealed, including the ones that look like an alias or noise at a glance, but later may prove to be a real variation. After the selection of the initial candidates, the algorithm performs a complete pass through all their possible combinations and computes the rigorous multifrequency statistical significance for each such frequency tuple. The largest combinations that still survived this thresholding procedure represent the outcome of the analysis. The parallel computing on a graphics processing unit (GPU) is implemented through CUDA and brings a significant performance increase. It is still possible to run FREDEC solely on CPU in the traditional single-threaded mode, when no suitable GPU device is available. To verify the practical applicability of our algorithm, we apply it to an artificial time series as well as to some real-life exoplanetary radial-velocity data. We demonstrate that FREDEC can successfully reveal several known exoplanets. Moreover, it detected a new 9.8-day variation in the Lick data for the five-planet system of 55 Cnc. It might indicate the existence of a small sixth planet in the 3:2 commensurability with the planet 55 Cnc b, although this detection is model-dependent and still needs a detailed verification. [Copyright &y& Elsevier]

Abstract: We present PlanetPack, a new software tool that we developed to facilitate and standardize the advanced analysis of radial velocity (RV) data for the goal of exoplanets detection, characterization, and basic dynamical -body simulations. PlanetPack is a command-line interpreter, that can run either in an interactive mode or in a batch mode of automatic script interpretation. Its major abilities include: (i) advanced RV curve fitting with the proper maximum-likelihood treatment of unknown RV jitter; (ii) user-friendly multi-Keplerian as well as Newtonian -body RV fits; (iii) use of more efficient maximum-likelihood periodograms that involve the full multi-planet fitting (sometimes called as “residual” or “recursive” periodograms); (iv) easily calculatable parametric 2D likelihood function level contours, reflecting the asymptotic confidence regions; (v) fitting under some useful functional constraints is user-friendly; (vi) basic tasks of short- and long-term planetary dynamical simulation using a fast Everhart-type integrator based on Gauss–Legendre spacings; (vii) fitting the data with red noise (auto-correlated errors); (viii) various analytical and numerical methods for the tasks of determining the statistical significance. It is planned that further functionality may be added to PlanetPack in the future. During the development of this software, a lot of effort was made to improve the calculational speed, especially for CPU-demanding tasks. PlanetPack was written in pure C++ (standard of 1998/2003), and is expected to be compilable and useable on a wide range of platforms. [Copyright &y& Elsevier]

Abstract: Detection of a signal hidden by noise within a time series is an important problem in many astronomical searches, i.e. for light curves containing the contributions of periodic/semi-periodic components due to rotating objects and all other astrophysical time-dependent phenomena. One of the most popular tools for use in such studies is the periodogram, whose use in an astronomical context is often not trivial. The optimal statistical properties of the periodogram are lost in the case of irregular sampling of signals, which is a common situation in astronomical experiments. Parts of these properties are recovered by the Lomb–Scargle (LS) technique, but at the price of theoretical difficulties, that can make its use unclear, and of algorithms that require the development of dedicated software if a fast implementation is necessary. Such problems would be irrelevant if the LS periodogram could be used to significantly improve the results obtained by approximated but simpler techniques. In this work we show that in many astronomical applications, simpler techniques provide results similar to those obtainable with the LS periodogram. The meaning of the Nyquist frequency is also discussed in the case of irregular sampling. [Copyright &y& Elsevier]

Abstract: In this study, we present the unpublished flare data collected from 222 flares detected in the B band observations of five stars and the results derived by statistical analysis and modeling of these data. Six basic properties have been found with a statistical analysis method applied to all models and analyses for the flares detected in the B band observation of UV Ceti type stars. We have also compared the U and B bands with the analysis results. This comparison allowed us to evaluate the methods used in the analyses. The analyses provided the following results. (1) The flares were separated into two types, fast and slow flares. (2) The mean values of the equivalent durations of the slow and the fast flares differ by a factor of 16.2±3.7. (3) Regardless of the total flare duration, the maximum flare energy can reach a different Plateau level for each star. (4) The Plateau values of EV Lac and EQ Peg are higher than the others. (5) The minimum values of the total flare duration increase toward the later spectral types. This value is called the Half-Life value in models. (6) Both the maximum flare rise times and the total flare duration obtained from the observed flares decrease toward the later spectral types. [Copyright &y& Elsevier]

Abstract: Beginning with the 2002 discovery of the “Amati Relation” of GRB spectra, there has been much interest in the possibility that this and other correlations of GRB phenomenology might be used to make GRBs into standard candles. One recurring apparent difficulty with this program has been that some of the primary observational quantities to be fit as “data” – to wit, the isotropic-equivalent prompt energy E iso and the collimation-corrected “total” prompt energy E γ – depend for their construction on the very cosmological models that they are supposed to help constrain. This is the so-called “circularity problem” of standard candle GRBs. This paper is intended to point out that the circularity problem is not in fact a problem at all, except to the extent that it amounts to a self-inflicted wound. It arises essentially because of an unfortunate choice of data variables – “source-frame” variables such as E iso , which are unnecessarily encumbered by cosmological considerations. If, instead, the empirical correlations of GRB phenomenology which are formulated in source-variables are mapped to the primitive observational variables (such as fluence) and compared to the observations in that space, then all taint of circularity disappears. I also indicate here a set of procedures for encoding high-dimensional empirical correlations (such as between E iso , , and ) in a “Gaussian Tube” smeared model that includes both the correlation and its intrinsic scatter, and how that source-variable model may easily be mapped to the space of primitive observables, to be convolved with the measurement errors and fashioned into a likelihood. I discuss the projections of such Gaussian tubes into sub-spaces, which may be used to incorporate data from GRB events that may lack some element of the data (for example, GRBs without ascertained jet-break times). In this way, a large set of inhomogeneously observed GRBs may be assimilated into a single analysis, so long as each possesses at least two correlated data attributes. [Copyright &y& Elsevier]

Abstract: With the availability of multi-wavelength, multi-scale and multi-epoch astronomical catalogues, the number of features to describe astronomical objects has increases. The better features we select to classify objects, the higher the classification accuracy is. In this paper, we have used data sets of stars and quasars from near-infrared band and radio band. Then best-first search method was applied to select features. For the data with selected features, the algorithm of decision table was implemented. The classification accuracy is more than 95.9%. As a result, the feature selection method improves the effectiveness and efficiency of the classification method. Moreover the result shows that decision table is robust and effective for discrimination of celestial objects and used for preselecting quasar candidates for large survey projects. [Copyright &y& Elsevier]

Abstract: The automated classification of objects from large catalogs or survey projects is an important task in many astronomical surveys. Faced with various classification algorithms, astronomers should select the method according to their requirements. Here we describe several kinds of decision trees for finding active objects by multi-wavelength data, such as REPTree, Random Tree, Decision Stump, Random Forest, J48, NBTree, AdTree. All decision tree approaches investigated are in the WEKA package. The classification performance of the methods is presented. In the process of classification by decision tree methods, the classification rules are easily obtained, moreover these rules are clear and easy to understand for astronomers. As a result, astronomers are inclined to prefer and apply them, thus know which attributes are important to discriminate celestial objects. The experimental results show that when various decision trees are applied in discriminating active objects (quasars, BL Lac objects and active galaxies) from non-active objects (stars and galaxies), ADTree is the best only in terms of accuracy, Decision Stump is the best only considering speed, J48 is the optimal choice considering both accuracy and speed. [Copyright &y& Elsevier]

Abstract: The measurements of pulsar frequency second derivatives have shown that they are 102–106 times larger than expected for standard pulsar spin-down law. Moreover, the second derivatives as well as braking indices are even negative for about half the pulsars. We explain these paradoxical results on the basis of the statistical analysis of the rotational parameters ν, and of the subset of 295 pulsars taken mostly from the ATNF database. We have found a strong correlation between and for both (correlation coefficient r ≈0.9) and (r ≈0.85), as well as between ν and (r ≈0.6–0.7). We interpret these dependencies as evolutionary ones due to being nearly proportional to the pulsars’ age. The derived statistical relations as well as “anomalous” values of are well described by assuming the existence of long-time variations of the spin-down rate. The pulsar frequency evolution, therefore, consists of secular change of ν ev(t), and according to the power law with n ≈5, the irregularities, observed within the timespan as timing noise, and the non-monotonous variations on the timescale of several tens of years, which is larger than that of the timespan. It is possible that the nature of long-term variations is similar to that of short-term ones. The idea of non-constant secular pulsars’ braking index n is also analysed. [Copyright &y& Elsevier]

We report on observations of two bright Trans-Neptunian Objects (TNOs)—1999 TC36 and 1998 SN165—during two observational campaigns, as part of the Meudon Multicolor Survey of Outer Solar System Objects. V−J color was measured for 1999 TC36 (V−J=2.34±0.18), which combined with previous measured colors in the visible, indicate a red reflectivity spectrum at all wavelengths. Photometric V-band lightcurves were taken for both objects over a time span of around 8 h. We have determined a possible rotational period of P=10.1±0.8 h for 1998 SN165, making it the seventh TNO with an estimated period. From its lightcurve variation of Δm=0.151−0.030+0.022, we have inferred an asymmetry ratio of a/b≥1.148−0.031+0.024. For 1999 TC36, we did not detect any rotational period or periodic signal variation within the uncertainties, but the analysis of its lightcurve hints to a slight systematic magnitude decrease. [Copyright &y& Elsevier]

Transient and variable phenomena in astrophysical sources are of particular importance to understand the underlying gamma-ray emission processes. In the very-high energy gamma-ray domain, transient and variable sources are related to charged particle acceleration processes that could for instance help understanding the origin of cosmic-rays. The imaging atmospheric Cherenkov technique used for gamma-ray astronomy above ~ 100 GeV is well suited for detecting such events. However, the standard analysis methods are not optimal for such a goal and more sensitive methods are specifically developed in this publication. The sensitivity improvement could therefore be helpful to detect brief and faint transient sources such as Gamma-Ray Bursts. [ABSTRACT FROM AUTHOR]

Pulsar candidate analysis pipelines have historically been comprised of bespoke software systems, supporting the off-line study of data. However modern data acquisition systems are making off-line analyses impractical, as increasing candidate volumes become prohibitively expensive to retain. To maintain processing capabilities when off-line analysis becomes infeasible due to cost, requires a shift to on-line data processing. This paper makes four contributions facilitating this shift relevant to the search for radio pulsars: (i) it characterises the key components of a pulsar search candidate processing pipeline, (ii) it examines the feasibility of implementing on-line candidate filtering via existing tools, (iii) explores the problems preventing an easy transition to on-line filtering, and finally (iv) presents a new prototype filtering pipeline capable of overcoming such problems. Realised using Commercial off-the-shelf (COTS) software components, the deployable system is open source, simple, scalable, and cheap to produce. It has the potential to achieve candidate filtering design requirements for the Square Kilometre Array (SKA), illustrated via testing under simulated SKA loads. [ABSTRACT FROM AUTHOR]