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  • Towards an astronomical science platform: Experiences and lessons learned from Chinese Virtual Observatory
    Astron. Comput. (IF 2.76) Pub Date : 2020-05-26
    Chenzhou Cui; Yihan Tao; Changhua Li; Dongwei Fan; Jian Xiao; Boliang He; Shanshan Li; Ce Yu; Linying Mi; Yunfei Xu; Jun Han; Sisi Yang; Yongheng Zhao; Yanjie Xue; Jinxin Hao; Liang Liu; Xiao Chen; Junyi Chen; Hailong Zhang

    In the era of big data astronomy, next generation telescopes and large sky surveys produce data sets at the TB or even PB level. Due to their large data volumes, these astronomical data sets are extremely difficult to transfer and analyze using personal computers or small clusters. In order to offer better access to data, data centers now generally provide online science platforms that enable analysis

  • Efficient Fermi source identification with machine learning methods
    Astron. Comput. (IF 2.76) Pub Date : 2020-05-22
    H.B. Xiao; H.T. Cao; J.H. Fan; D. Costantin; G.Y. Luo; Z.Y. Pei

    In this work, Machine Learning (ML) methods are used to efficiently identify the unassociated sources and the Blazar Candidate of Uncertain types (BCUs) in the Fermi-LAT Third Source Catalog (3FGL). The aims are twofold: (1) to distinguish the Active Galactic Nuclei (AGNs) from others (non-AGNs) in the unassociated sources; (2) to identify BCUs into BL Lacertae objects (BL Lacs) or Flat Spectrum Radio

  • CosmoHub: Interactive exploration and distribution of astronomical data on Hadoop
    Astron. Comput. (IF 2.76) Pub Date : 2020-05-16
    P. Tallada; J. Carretero; J. Casals; C. Acosta-Silva; S. Serrano; M. Caubet; F.J. Castander; E. César; M. Crocce; M. Delfino; M. Eriksen; P. Fosalba; E. Gaztañaga; G. Merino; C. Neissner; N. Tonello

    We present CosmoHub (https://cosmohub.pic.es), a web application based on Hadoop to perform interactive exploration and distribution of massive cosmological datasets. Recent Cosmology seeks to unveil the nature of both dark matter and dark energy mapping the large-scale structure of the Universe, through the analysis of massive amounts of astronomical data, progressively increasing during the last

  • DeepMerge: Classifying high-redshift merging galaxies with deep neural networks
    Astron. Comput. (IF 2.76) Pub Date : 2020-05-16
    A. Ćiprijanović; G.F. Snyder; B. Nord; J.E.G. Peek

    We investigate and demonstrate the use of convolutional neural networks (CNNs) for the task of distinguishing between merging and non-merging galaxies in simulated images, and for the first time at high redshifts (i.e., z=2). We extract images of merging and non-merging galaxies from the Illustris-1 cosmological simulation and apply observational and experimental noise that mimics that from the Hubble

  • Development and application of an HDF5 schema for SKA-scale image cube visualization
    Astron. Comput. (IF 2.76) Pub Date : 2020-05-15
    A. Comrie; A. Pińska; R. Simmonds; A.R. Taylor

    In this paper, we describe an HDF5 schema created to support the efficient visualization of the large image cubes that will be produced by SKA Phase 1 and precursor radio telescopes. We demonstrate how the “HDF5-IDIA” schema’s features can improve the performance of visualization software, using both low-level metrics and real-world tests of the schema’s implementation in CARTA, an image viewer that

  • Radio-astronomical imaging on graphics processors
    Astron. Comput. (IF 2.76) Pub Date : 2020-05-13
    B. Veenboer; J.W. Romein

    Realizing the next generation of radio telescopes such as the Square Kilometre Array (SKA) requires both more efficient hardware and algorithms than today’s technology provides. The image-domain gridding (IDG) algorithm is a novel approach towards solving the most compute-intensive parts of creating sky images: gridding and degridding. It alleviates the performance bottlenecks of traditional AW-projection

  • Abelian-Higgs cosmic string evolution with CUDA
    Astron. Comput. (IF 2.76) Pub Date : 2020-05-12
    J.R.C.C.C. Correia; C.J.A.P. Martins

    Topological defects form at cosmological phase transitions by the Kibble mechanism, with cosmic strings—one-dimensional defects—being the most studied example. A rigorous analysis of their astrophysical consequences is limited by the availability of accurate numerical simulations, and therefore by hardware resources and computation time. Improving the speed and efficiency of existing codes is therefore

  • Comparison of automatic methods to detect sunspots in the Coimbra Observatory spectroheliograms
    Astron. Comput. (IF 2.76) Pub Date : 2020-05-06
    S. Carvalho; S. Gomes; T. Barata; A. Lourenço; N. Peixinho

    The Astronomical Observatory of the University of Coimbra has a huge collection of solar images, acquired daily since 1926. From the beginning, only spectroheliograms in the CaiiK line have been recorded, and since 1989 in the Hα line also. Such dataset requires efficient tools to detect and analyze solar activity features. The objective of this work is to create a tool that allows to automatically

  • Introducing PyCross: PyCloudy Rendering Of Shape Software for pseudo 3D ionisation modelling of nebulae
    Astron. Comput. (IF 2.76) Pub Date : 2020-04-29
    K. Fitzgerald; E.J. Harvey; N. Keaveney; M.P. Redman

    Research into the processes of photoionised nebulae plays a significant part in our understanding of stellar evolution. It is extremely difficult to visually represent or model ionised nebula, requiring astronomers to employ sophisticated modelling code to derive temperature, density and chemical composition. Existing codes are available that often require steep learning curves and produce models derived

  • Classification of galaxy color images using quaternion polar complex exponential transform and binary Stochastic Fractal Search
    Astron. Comput. (IF 2.76) Pub Date : 2020-04-28
    K.M. Hosny; M.A. Elaziz; I.M. Selim; M.M. Darwish

    Galaxies’ studies play an important role in the astronomic. Accurate classification of these galaxies enables scientists to understand the formation and evolution of the Universe. During the last decades, there have been several methods applied to classify the galaxy images. However, these methods encounter three big challenges. First, most existing methods converted the color images of galaxies into

  • Astroalign: A Python module for astronomical image registration
    Astron. Comput. (IF 2.76) Pub Date : 2020-04-28
    M. Beroiz; J.B. Cabral; B. Sanchez

    We present an algorithm implemented in the Astroalign Python module for image registration in astronomy. Our module does not rely on WCS information and instead matches three-point asterisms (triangles) on the images to find the most accurate linear transformation between them. It is especially useful in the context of aligning images prior to stacking or performing difference image analysis. Astroalign

  • IVOA HiPS implementation in the framework of WorldWide Telescope
    Astron. Comput. (IF 2.76) Pub Date : 2020-03-20
    Y. Xu; C. Cui; D. Fan; S. Li; C. Li; J. Han; L. Mi; B. He; H. Yang; Y. Tao; S. Yang; L. He

    The WorldWide Telescope(WWT) is a scientific visualization platform which can browse deep space images, star catalogs, and planetary remote sensing data from different observation facilities in a three-dimensional virtual scene. First launched and then open-sourced by Microsoft Research, the WWT is now managed by the American Astronomical Society (AAS). Hierarchical Progressive Survey (HiPS) is an

  • Exo-MerCat: A merged exoplanet catalog with Virtual Observatory connection
    Astron. Comput. (IF 2.76) Pub Date : 2020-02-12
    E. Alei; R. Claudi; A. Bignamini; M. Molinaro

    The heterogeneity of papers dealing with the discovery and characterization of exoplanets makes every attempt to maintain a uniform exoplanet catalog almost impossible. Four sources currently available online (NASA Exoplanet Archive, Exoplanet Orbit Database, Exoplanet Encyclopaedia, and Open Exoplanet Catalogue) are commonly used by the community, but they can hardly be compared, due to discrepancies

  • SKIRT 9: Redesigning an advanced dust radiative transfer code to allow kinematics, line transfer and polarization by aligned dust grains
    Astron. Comput. (IF 2.76) Pub Date : 2020-04-08
    P. Camps; M. Baes

    The open source SKIRT Monte Carlo radiative transfer code has been used for more than 15 years to model the interaction between radiation and dust in various astrophysical systems. In this work, we present version 9 of the code, which has been substantially redesigned to support long-term objectives. We invite interested readers to participate in the development, testing and application of new features

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