This paper discusses an autoregressive model for the analysis of irregularly observed time series. The properties of this model are studied and a maximum likelihood estimation procedure is proposed. The finite sample performance of this estimator is assessed by Monte Carlo simulations, showing accurate estimators. We implement this model to the residuals after fitting an harmonic model to light-curves from periodic variable stars from the Optical Gravitational Lensing Experiment (OGLE) and Hipparcos surveys, showing that the model can identify time dependency structure that remains in the residuals when, for example, the period of the light-curves was not properly estimated.

In the present work we investigate the possible relationship of long-period comets with five large and distant trans-Neptunian bodies (Sedna, Eris, 2007 OR10, 2012 VP113 and 2008 ST291) in order to determine the probability of the transfer of a part of these kind of comets to the inner of the Solar System. To identify such relationships, we studied the relative positions of the comet orbits and listed TNOs. Using numerical integration methods, we examined dynamical evolution of the comets and have found one encounter of comet C/1861J1 and Eris.

Using recent data from photometric monitoring and data from the photographic plate archives we aim to study, the long-term photometric behavior of FUors. The construction of the historical light curves of FUors could be very important for determining the beginning of the outburst, the time to reach the maximum light, the rate of increase and decrease in brightness, the pre-outburst variability of the star. Our CCD photometric observations were performed with the telescopes of the Rozhen (Bulgaria) and Skinakas (Crete, Greece) observatories. Most suitable for long-term photometric study are the plate archives of the big Schmidt telescopes, as the telescopes at Kiso Observatory, Asiago Observatory, Palomar Observatory and others. In comparing our results with light curves of the well-studied FUors, we conclude that every new FUor object shows different photometric behavior. Each known FUor has a different rate of increase and decrease in brightness and a different light curve shape.

We present the results of the search of variable sources and transient events in the archive data of the sky surveys conducted on 3.9 GHz on the RATAN-600 radio telescope of the Special Astrophysical Observatory of the Russian Academy of Sciences (SAO RAS) in 1980-1994. 17% of the total studied sources can be attributed to the variables in radio range. About half of them has significant variations in optical brightness according to the data of the catalogs. At the level of 3-5 r.m.s. we found three transient events. Two weak events probably associated with AGN activities or with cataclysmic events such as GRB and a supernova flash. The nature of the third event has not been established. According to our estimation the surface density of radio transients is 0.03 on one square angular degree with the detection level 8–11 mJy on 3.94 GHz.

We developed a method that allows to classify the light curves of eclipsing binaries of the W UMa type (EW) with respect to their intrinsic variability. The algorithm measures several features of light curves, such as the amplitude of the O’Connell effect, the separation and location of maxima brightness as well as depths of the minima in subsequent orbital periods. This method is capable of distinguishing systems with presumed magnetic activity present from these without it, as well as recognizing systems with starspots migration and those with other types of intrinsic variability manifestation. The classification is done in an automatic way without a time consuming, visual inspection of light curves.

Leading-edge telescopes such as the Atacama Large Millimeter and sub-millimeter Array (ALMA), and near-future ones, are capable of imaging the same sky area at hundreds-to-thousands of frequencies with both high spectral and spatial resolution. This provides unprecedented opportunities for discovery about the spatial, kinematical and compositional structure of sources such as molecular clouds or protoplanetary disks, and more. However, in addition to enormous volume, the data also exhibit unprecedented complexity, mandating new approaches for extracting and summarizing relevant information. Traditional techniques such as examining images at selected frequencies become intractable while tools that integrate data across frequencies or pixels (like moment maps) can no longer fully exploit and visualize the rich information. We present a neural map-based machine learning approach that can handle all spectral channels simultaneously, utilizing the full depth of these data for discovery and visualization of spectrally homogeneous spatial regions (spectral clusters) that characterize distinct kinematic behaviors. We demonstrate the effectiveness on an ALMA image cube of the protoplanetary disk HD142527. The tools we collectively name “NeuroScope” are efficient for “Big Data” due to intelligent data summarization that results in significant sparsity and noise reduction. We also demonstrate a new approach to automate our clustering for fast distillation of large data cubes.

The VIALACTEA project brings to a common forum the major new-generation surveys of the Milky Way Galactic Plane from 1μm to the radio, both in thermal continuum and in atomic and molecular lines, to attack in a systematic way the characterization of the Milky Way as a star formation engine. Images, catalogues, spectroscopic datacubes and radiative transfer models of the Spectral Energy Distributions (SEDs) of sites of star formation have been incorporated and indexed in the VIALACTEA Knowledge Base (VLKB). The VLKB consists of a combination of a relational database where the VIALACTEA data and metadata are homogenised and stored, and a filesystem-based stored information. This infrastructure allowed, among others, the generation of extensive catalogue for compact sources and extended structures in the Galactic Plane, the implementation of data-mining algorithms for the band-merging of multiwavelength data and expert systems for the automated analysis of molecular line surveys to extract critical kinematical information and derive distances using Galaxy rotation curves and new 3D extinction maps. A new VIALACTEA 3D Visual Analytics interface has been developed that provides integrated access and analysis of continuum and spectroscopic images together with catalogue data directly interfacing with the VLKB.

With new catalogues arriving such as the Gaia DR1, containing more than a billion objects, new methods of handling and visualizing these data volumes are needed. We show that by calculating statistics on a regular (N-dimensional) grid, visualizations of a billion objects can be done within a second on a modern desktop computer. This is achieved using memory mapping of hdf5 files together with a simple binning algorithm, which are part of a Python library called vaex. This enables efficient exploration or large datasets interactively, making science exploration of large catalogues feasible. Vaex is a Python library and an application, which allows for interactive exploration and visualization. The motivation for developing vaex is the catalogue of the Gaia satellite, however, vaex can also be used on SPH or N-body simulations, any other (future) catalogues such as SDSS, Pan-STARRS, LSST, etc. or other tabular data. The homepage for vaex is http://vaex.astro.rug.nl.

Upcoming HI surveys will deliver such large datasets that automated processing using the full 3-D information to find and characterize HI objects is unavoidable. Full 3-D visualization is an essential tool for enabling qualitative and quantitative inspection and analysis of the 3-D data, which is often complex in nature. Here we present SlicerAstro, an open-source extension of 3DSlicer, a multi-platform open source software package for visualization and medical image processing, which we developed for the inspection and analysis of HI spectral line data. We describe its initial capabilities, including 3-D filtering, 3-D selection and comparative modelling.

Radio survey datasets comprise an increasing number of individual observations stored as sets of multidimensional data. In large survey projects, astronomers commonly face limitations regarding: 1) interactive visual analytics of sufficiently large subsets of data; 2) synchronous and asynchronous collaboration; and 3) documentation of the discovery workflow. To support collaborative data inquiry, we present encube, a large-scale comparative visual analytics framework. encube can utilise advanced visualization environments such as the CAVE2 (a hybrid 2D and 3D virtual reality environment powered with a 100 Tflop/s GPU-based supercomputer and 84 million pixels) for collaborative analysis of large subsets of data from radio surveys. It can also run on standard desktops, providing a capable visual analytics experience across the display ecology. encube is composed of four primary units enabling compute-intensive processing, advanced visualisation, dynamic interaction, parallel data query, along with data management. Its modularity will make it simple to incorporate astronomical analysis packages and Virtual Observatory capabilities developed within our community. We discuss how encube builds a bridge between high-end display systems (such as CAVE2) and the classical desktop, preserving all traces of the work completed on either platform – allowing the research process to continue wherever you are.

This paper presents the VESPA (Virtual European Solar and Planetary Access) activity, developed in the context of the Europlanet 2020 Horizon project, aimed at providing tools for analysis and visualization of planetary data provided by space missions. In particular, the activity is focused on minor bodies of the Solar System.The structure of the computation node, the algorithms developed for analysis of planetary surfaces and cometary comae and the tools for data visualization are presented.

Currently large sky area spectral surveys like SDSS, 2dF, and LAMOST, using the new generation of telescopes and observatories, have provided massive spectral data sets for astronomical research. Most of the data can be automatically handled with pipelines, but visually inspection by human eyes is still necessary in several situations, like low SNR spectra, QSO recognition and peculiar spectra mining. Using ASERA, A Spectrum Eye Recognition Assistant, we can set up a team spectral inspection platform. On a preselected spectral data set, members of a team can individually view spectra one by one, find the best match template and estimate the redshift. Results from different members will be gathered and merged to raise the team work efficiency. ASERA mainly targets the spectra of SDSS and LAMOST fits data formats. Other formats can be supported with some conversion. Spectral templates from SDSS and LAMOST pipelines are embedded and users can easily add their own templates. Convenient cross identification interfaces with SDSS, SIMBAD, VIZIER, NED and DSS are also provided. An application example targeting finding strong emission line spectra from LAMOST DR2 is presented.

In the current data-driven science era, it is needed that data analysis techniques has to quickly evolve to face with data whose dimensions has increased up to the Petabyte scale. In particular, being modern astrophysics based on multi-wavelength data organized into large catalogues, it is crucial that the astronomical catalog cross-matching methods, strongly dependant from the catalogues size, must ensure efficiency, reliability and scalability. Furthermore, multi-band data are archived and reduced in different ways, so that the resulting catalogues may differ each other in formats, resolution, data structure, etc, thus requiring the highest generality of cross-matching features. We present C3 (Command-line Catalogue Cross-match), a multi-platform application designed to efficiently cross-match massive catalogues from modern surveys. Conceived as a stand-alone command-line process or a module within generic data reduction/analysis pipeline, it provides the maximum flexibility, in terms of portability, configuration, coordinates and cross-matching types, ensuring high performance capabilities by using a multi-core parallel processing paradigm and a sky partitioning algorithm.

After its first implementation in 2003 the Astro-WISE technology has been rolled out in several European countries and is used for the production of the KiDS survey data. In the multi-disciplinary Target initiative this technology, nicknamed WISE technology, has been further applied to a large number of projects. Here, we highlight the data handling of other astronomical applications, such as VLT-MUSE and LOFAR, together with some non-astronomical applications such as the medical projects Lifelines and GLIMPS; the MONK handwritten text recognition system; and business applications, by amongst others, the Target Holding.

We describe some of the most important lessons learned and describe the application of the data-centric WISE type of approach to the Science Ground Segment of the Euclid satellite.

The astrophysics community uses different tools for computational tasks such as complex systems simulations, radiative transfer calculations or big data. Programming languages like Fortran, C or C++ are commonly present in these tools and, generally, the language choice was made based on the need for performance. However, this comes at a cost: safety. For instance, a common source of error is the access to invalid memory regions, which produces random execution behaviors and affects the scientific interpretation of the results.

In 2015, Mozilla Research released the first stable version of a new programming language named Rust. Many features make this new language attractive for the scientific community, it is open source and it guarantees memory safety while offering zero-cost abstraction.

We explore the advantages and drawbacks of Rust for astrophysics by re-implementing the fundamental parts of Mercury-T, a Fortran code that simulates the dynamical and tidal evolution of multi-planet systems.

Nowadays there is no field research which is not flooded with data. Among the sciences, astrophysics has always been driven by the analysis of massive amounts of data. The development of new and more sophisticated observation facilities, both ground-based and spaceborne, has led data more and more complex (Variety), an exponential growth of both data Volume (i.e., in the order of petabytes), and Velocity in terms of production and transmission. Therefore, new and advanced processing solutions will be needed to process this huge amount of data. We investigate some of these solutions, based on machine learning models as well as tools and architectures for Big Data analysis that can be exploited in the astrophysical context.

In this article we described CoLiTec software for full automated frames processing. CoLiTec software allows processing the Big Data of observation results as well as processing of data that is continuously formed during observation. The scope of solving tasks includes frames brightness equalization, moving objects detection, astrometry, photometry, etc. Along with the high efficiency of Big Data processing CoLiTec software also ensures high accuracy of data measurements. A comparative analysis of the functional characteristics and positional accuracy was performed between CoLiTec and Astrometrica software. The benefits of CoLiTec used with wide field and low quality frames were observed. The efficiency of the CoLiTec software was proved by about 700.000 observations and over 1.500 preliminary discoveries.

Astronomy cloud computing environment is a cyber-Infrastructure for Astronomy Research initiated by Chinese Virtual Observatory (China-VO) under funding support from NDRC (National Development and Reform commission) and CAS (Chinese Academy of Sciences). Based on virtualization technology, astronomy cloud computing environment was designed and implemented by China-VO team. It consists of five distributed nodes across the mainland of China. Astronomer can get compuitng and storage resource in this cloud computing environment. Through this environments, astronomer can easily search and analyze astronomical data collected by different telescopes and data centers , and avoid the large scale dataset transportation.

The FP-7 (Framework Programme 7 of the European Union) PERICLES project addresses the life-cycle of large and complex data sets to cater for the evolution of context of data sets and user communities, including groups unanticipated when the data was created. Semantics of data sets are thus also expected to evolve and the project includes elements which could address the reuse of data sets at periods where the data providers and even their institutions are not available any more. This paper presents the PERICLES science case with the example of the SOLAR (SOLAR monitoring observatory) payload on International Space Station-Columbus.

The aim of this paper is to introduce the software and astronomical web-services developed in frame of the Ukraine Virtual Observatory (UkrVO) and VIrtual Roentgen and Gamma Observatory (VIRGO) to the broad astronomical community. We report briefly on such web-services as the public databases “Sky Maps” of X-ray observations by XMM-Newton, the Earth Orbital Parameters for International Earth Rotation System, and the UkrVO Joint Digitized Archive of astroplates. We refer to such software as the “Multi-Column View”, “Variable Stars Calculator”, “FrameSmooth” for variable star’s research and “Collection Light Technology” (CoLiTec) software for search and discovery of new Solar System bodies.