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  • SPARTAN: Maximizing the use of spectro-photometric observational data during template fitting
    Astron. Comput. (IF 1.854) Pub Date : 2020-10-14
    R. Thomas

    SPARTAN [Spectroscopic And photometRic fitting Tool for Astronomical aNalysis] is a tool designed to perform the fitting of galaxy observations either using photometry and low resolution spectroscopy separately or simultaneously. Based on a grid search χ2 fitting method, SPARTAN was tailored to UV-to-NIR data and designed for well calibrated data. The first version of this tool allows the use of the

  • Astro Space Locator — A software package for VLBI data processing and reduction
    Astron. Comput. (IF 1.854) Pub Date : 2020-09-23
    S.F. Likhachev; I.A. Girin; V. Yu. Avdeev; A.S. Andrianov; M.N. Andrianov; V.I. Kostenko; V.A. Ladygin; A.O. Lyakhovets; I.D. Litovchenko; A.G. Rudnitskiy; M.A. Shchurov; N.D. Utkin; V.A. Zuga

    The article describes the main features and algorithms of Astro Space Locator (ASL) software package. This high performance package has a user-friendly graphical interface and it is used for VLBI data processing and reduction. ASL supports data editing, calibration, multi-frequency analysis, standard and multi-frequency VLBI imaging.

  • Optical follow-up of gravitational wave triggers with DECam during the first two LIGO/VIRGO observing runs
    Astron. Comput. (IF 1.854) Pub Date : 2020-09-18
    K. Herner; J. Annis; D. Brout; M. Soares-Santos; R. Kessler; M. Sako; R. Butler; Z. Doctor; A. Palmese; S. Allam; D.L. Tucker; F. Sobreira; B. Yanny; H.T. Diehl; J. Frieman; N. Glaeser; A. Garcia; N.F. Sherman; Y. Zhang

    Gravitational wave (GW) events detectable by LIGO and Virgo have several possible progenitors, including black hole mergers, neutron star mergers, black hole–neutron star mergers, supernovae, and cosmic string cusps. A subset of GW events is expected to produce electromagnetic (EM) emission that, once detected, will provide complementary information about their astrophysical context. To that end, the

  • Clustering-informed cinematic astrophysical data visualization with application to the Moon-forming terrestrial synestia
    Astron. Comput. (IF 1.854) Pub Date : 2020-09-09
    P.D. Aleo, S.J. Lock, D.J. Cox, S.A. Levy, J.P. Naiman, A.J. Christensen, K. Borkiewicz, R. Patterson

    Scientific visualization tools are currently not optimized to create cinematic, production-quality representations of numerical data for the purpose of science communication. In our pipeline Estra, we outline a step-by-step process from a raw simulation into a finished render as a way to teach non-experts in the field of visualization how to achieve production-quality outputs on their own. We demonstrate

  • Understanding the human in the design of cyber-human discovery systems for data-driven astronomy
    Astron. Comput. (IF 1.854) Pub Date : 2020-09-02
    C.J. Fluke, S.E. Hegarty, C.O.-M. MacMahon

    High-quality, useable, and effective software is essential for supporting astronomers in the discovery-focused tasks of data analysis and visualisation. As the volume, and perhaps more crucially, the velocity of astronomical data grow, the role of the astronomer is changing. There is now an increased reliance on automated and autonomous discovery and decision-making workflows rather than visual inspection

  • Data Lab—A community science platform
    Astron. Comput. (IF 1.854) Pub Date : 2020-08-29
    R. Nikutta; M. Fitzpatrick; A. Scott; B.A. Weaver

    Data Lab is an open-access science platform developed and operated by the Community and Science Data Center (CSDC) at NSF’s National Optical-Infrared Astronomy Research Laboratory (NOIRLab). It serves public photometric survey datasets, provides interactive and programmatic data access, and SQL/ADQL query capabilities via TAP. Users also receive generous storage allocations with VOSpace and MyDB, co-located

  • Mask galaxy: Morphological segmentation of galaxies
    Astron. Comput. (IF 1.854) Pub Date : 2020-08-28
    H. Farias, D. Ortiz, G. Damke, M. Jaque Arancibia, M. Solar

    The classification of galaxies based on their morphology is instrumental for the understanding of galaxy formation and evolution. This, in addition to the ever-growing digital astronomical datasets, has motivated the application of advanced computer vision techniques, such as Deep Learning. However, these models have not been implemented as single pipelines that replicate detection, segmentation and

  • SciServer: A science platform for astronomy and beyond
    Astron. Comput. (IF 1.854) Pub Date : 2020-08-25
    M. Taghizadeh-Popp, J.W. Kim, G. Lemson, D. Medvedev, M.J. Raddick, A.S. Szalay, A.R. Thakar, J. Booker, C. Chhetri, L. Dobos, M. Rippin

    We present SciServer, a science platform built and supported by the Institute for Data Intensive Engineering and Science at the Johns Hopkins University. SciServer builds upon and extends the SkyServer system of server-side tools that introduced the astronomical community to SQL (Structured Query Language) and has been serving the Sloan Digital Sky Survey catalog data to the public. SciServer uses

  • Comparison of data storage and analysis throughput in the light of high energy physics experiment MACE
    Astron. Comput. (IF 1.854) Pub Date : 2020-08-20
    D. Sarkar, Mahesh P., Padmini S., N. Chouhan, C. Borwankar, A.K. Bhattacharya, A.K. Tickoo, R.C. Rannot

    High Energy Physics (HEP) Experiments produce large amounts of data. The data produced in these experiments are in the range of terabytes and petabytes. The explosion of data has posed a challenge in data capture, storage, data integrity, searching, querying, visualization and analysis. This has led to the development of domain-specific file formats like FITS, HDF5, analysis frameworks like ROOT, storage

  • A versatile smoothed particle hydrodynamics code for graphic cards
    Astron. Comput. (IF 1.854) Pub Date : 2020-08-19
    C.M. Schäfer, O.J. Wandel, C. Burger, T.I. Maindl, U. Malamud, S.K. Buruchenko, R. Sfair, H. Audiffren, E. Vavilina, P.M. Winter

    We present the second release of the now open source smoothed particle hydrodynamics code miluphcuda. The code is designed to run on Nvidia CUDA capable devices. It handles one to three dimensional problems and includes modules to solve the equations for viscid and inviscid hydrodynamical flows, the equations of continuum mechanics using SPH, and self-gravity with a Barnes–Hut tree. The covered material

  • Predicting pulsar stars using a random tree boosting voting classifier (RTB-VC)
    Astron. Comput. (IF 1.854) Pub Date : 2020-08-12
    F. Rustam, A. Mehmood, S. Ullah, M. Ahmad, D. Muhammad Khan, G.S. Choi, B.-W. On

    The recent exponential growth in the data volume and number of identified pulsar stars is due to pulsar candidate search experiments and surveys. In this study, we investigated the existing methods and techniques used for pulsar prediction, such as applying filters based on pulsar observations, which can adversely affect the success of accurate pulsar prediction. Some of the existing methods are not

  • Space target extraction and detection for wide-field surveillance
    Astron. Comput. (IF 1.854) Pub Date : 2020-08-01
    D. Liu, X. Wang, Z. Xu, Y. Li, W. Liu

    A wide-field surveillance system with a long exposure time has a stronger capability of space target detection. However, it also produces some complicated situations that make it difficult to detect space targets; some stars appear as streak-like sources, countless object points, and possible discontinuous or nonlinear target trajectories. We present a space target detection method with high detection

  • Modeling transit and reflected light curves for non-spherical exoplanets
    Astron. Comput. (IF 1.854) Pub Date : 2020-07-23
    B. Carado, K.H. Knuth

    Newton was the first to provide a mathematical framework to describe the oblateness of the Earth, a framework later expended by Maclaurin, Jacobi, and Poincaré and applied to other astronomical objects. In the Solar System, the Jacobi triaxial ellipsoid has successfully described the shape of Haumea and Chariklo, while Ceres appears to be an oblate spheroid of the Maclaurin type. Beyond the Solar System

  • A cuBLAS-based GPU correlation engine for a low-frequency radio telescope
    Astron. Comput. (IF 1.854) Pub Date : 2020-07-18
    N. Ragoomundun, G.K. Beeharry

    A low-frequency array is being set up to observe the Deuterium hyperfine line at 327.4 MHz in Mauritius, in the southern hemisphere. The array will be used to measure the total power of the Deuterium emission in the local galaxy in order to determine the D/H abundance ratio. Radio astronomy is a compute intensive discipline. With the advent of large interferometers the digital processing is predominantly

  • Predictive ARIMA Model for coronal index solar cyclic data
    Astron. Comput. (IF 1.854) Pub Date : 2020-07-18
    M.F. Akhter, D. Hassan, S. Abbas

    Many solar activities, e.g. sunspots, Mg II indices, coronal index of solar activity, etc., are produced by solar magnetic fields that affect both, the solar atmosphere and heliosphere, including the terrestrial environment. The atmosphere of the sun is recognized by photosphere, as a visible solar disk and layers above it: chromosphere, transition zone and corona. The nature of solar activity can

  • PySAP: Python Sparse Data Analysis Package for multidisciplinary image processing
    Astron. Comput. (IF 1.854) Pub Date : 2020-06-25
    S. Farrens, A. Grigis, L. El Gueddari, Z. Ramzi, Chaithya G.R., S. Starck, B. Sarthou, H. Cherkaoui, P. Ciuciu, J.-L. Starck

    We present the open-source image processing software package PySAP (Python Sparse data Analysis Package) developed for the COmpressed Sensing for Magnetic resonance Imaging and Cosmology (COSMIC) project. This package provides a set of flexible tools that can be applied to a variety of compressed sensing and image reconstruction problems in various research domains. In particular, PySAP offers fast

  • Performance of CUDA-SHAPE on complex synthetic shapes and real data of asteroid (341843) 2008 EV5
    Astron. Comput. (IF 1.854) Pub Date : 2020-06-24
    M. Engels, S. Hudson, C. Magri

    CUDA-SHAPE is a GPU-accelerated algorithm for asteroid shape modeling based on the SHAPE modeling software which has been used in radar-based asteroid research over the past two decades. The new algorithm has been shown to be up to 19x faster on synthetic datasets of two scale model asteroids. In this paper, we apply CUDA-SHAPE to a third synthetic dataset of a more complex shape and to the dataset

  • A redistribution tool for long-term archive of astronomical observation data
    Astron. Comput. (IF 1.854) Pub Date : 2020-06-08
    C. Sun, C. Yu, C. Cui, B. He, J. Xiao, Z. Li, S. Tang, J. Sun

    Astronomical observation data require long-term preservation, and the rapid accumulation of observation data makes it necessary to consider the cost of long-term archive storage. In addition to low-speed disk-based online storage, optical disk or tape-based offline storage can be used to save costs. However, for astronomical research that requires historical data (particularly time-domain astronomy)

  • Towards an astronomical science platform: Experiences and lessons learned from Chinese Virtual Observatory
    Astron. Comput. (IF 1.854) Pub Date : 2020-05-26
    C. Cui, Y. Tao, C. Li, D. Fan, J. Xiao, B. He, S. Li, C. Yu, L. Mi, Y. Xu, J. Han, S. Yang, Y. Zhao, Y. Xue, J. Hao, L. Liu, X. Chen, J. Chen, H. 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 1.854) 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 1.854) 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 1.854) 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 1.854) 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 1.854) 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 1.854) 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 1.854) 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 1.854) 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 1.854) 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 1.854) 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

  • SKIRT 9: Redesigning an advanced dust radiative transfer code to allow kinematics, line transfer and polarization by aligned dust grains
    Astron. Comput. (IF 1.854) 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

  • IVOA HiPS implementation in the framework of WorldWide Telescope
    Astron. Comput. (IF 1.854) 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 1.854) 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

  • Optimal target assignment for massive spectroscopic surveys
    Astron. Comput. (IF 1.854) Pub Date : 2020-01-15
    M. Macktoobian, D. Gillet, J.-P. Kneib

    Robotics have recently contributed to cosmological spectroscopy to automatically obtain the map of the observable universe using robotic fiber positioners. For this purpose, an assignment algorithm is required to assign each robotic fiber positioner to a target associated with a particular observation. The assignment process directly impacts on the coordination of robotic fiber positioners to reach

  • NVST chromosphere data interference fringes removal based on NSCT and PCA
    Astron. Comput. (IF 1.854) Pub Date : 2020-01-15
    D.-J. Liu, S. Zheng, Y. Huang

    The New Vacuum Solar Telescope (NVST) has been affected by thin film interference since its operation, which makes interference fringes superimposed on its sun chromosphere data products. These fringes have a serious impact on the quantitative analysis and high-resolution reconstruction of solar fine structure observation. Although some methods have been adopted to remove the interference fringes in

  • Conditional density estimation tools in python and R with applications to photometric redshifts and likelihood-free cosmological inference
    Astron. Comput. (IF 1.854) Pub Date : 2020-01-13
    N. Dalmasso, T. Pospisil, A.B. Lee, R. Izbicki, P.E. Freeman, A.I. Malz

    It is well known in astronomy that propagating non-Gaussian prediction uncertainty in photometric redshift estimates is key to reducing bias in downstream cosmological analyses. Similarly, likelihood-free inference approaches, which are beginning to emerge as a tool for cosmological analysis, require a characterization of the full uncertainty landscape of the parameters of interest given observed data

  • A fully automated data center for the space-borne hard X-ray Compton polarimeter POLAR developed at PSI
    Astron. Comput. (IF 1.854) Pub Date : 2020-01-13
    H. Xiao, W. Hajdas, R. Marcinkowski

    Constant data inflow from the hard X-ray polarimeter POLAR onboard of the Chinese Space Laboratory TG-2 requires fully automated and safe guarded data processing. For this purpose, a dedicated data center was established at the Paul Scherrer Institute (PSI). This paper presents its design concept and structure, and describes the data flow and data products, data processing pipelines, quick-look utilities

  • Lenstool-HPC: A High Performance Computing based mass modelling tool for cluster-scale gravitational lenses
    Astron. Comput. (IF 1.854) Pub Date : 2019-12-24
    C. Schäfer, G. Fourestey, J.-P. Kneib

    With the upcoming generation of telescopes, cluster scale strong gravitational lenses will act as an increasingly relevant probe of cosmology and dark matter. The better resolved data produced by current and future facilities requires faster and more efficient lens modelling software. Consequently, we present Lenstool-HPC, a strong gravitational lens modelling and map generation tool based on High

  • MACE camera controller embedded software: Redesign for robustness and maintainability
    Astron. Comput. (IF 1.854) Pub Date : 2019-12-24
    S. Srivastava, A. Jain, P.M. Nair, P. Sridharan

    Control and monitoring software for data acquisition systems should have an efficient, reliable, and robust design comprising dual redundant communications, fault tolerant behavior, error handling, and recovery features as well as simple deployment and maintainability. The Major Atmospheric Cherenkov Experiment (MACE) telescope comprises many functionally diverse subsystems. The camera is one of its

  • Mega-Archive and the EURONEAR tools for data mining world astronomical images
    Astron. Comput. (IF 1.854) Pub Date : 2019-12-12
    O. Vaduvescu, L. Curelaru, M. Popescu

    The world astronomical image archives offer huge opportunities to time-domain astronomy sciences and other hot topics such as space defense, and astronomical observatories should improve this wealth and make it more accessible in the big data era. In 2010 we introduced the Mega-Archive database and the Mega-Precovery server for data mining images serendipitously containing Solar system bodies, with

  • SEDOBS: A tool to create simulated galaxy observations
    Astron. Comput. (IF 1.854) Pub Date : 2019-11-21
    R. Thomas

    SEDOBS is a python software designed to produce large samples of simulated galaxy observations. It allows for the creation of several types of mock observation such as photometry, spectroscopy, multi-spectroscopy and full spectro-photometric combinations. It has been primarily created to test galaxy template fitting method against any configuration of data. It has been designed to be user-friendly

  • CEESA meets machine learning: A Constant Elasticity Earth Similarity Approach to habitability and classification of exoplanets
    Astron. Comput. (IF 1.854) Pub Date : 2019-11-14
    S. Basak, S. Saha, A. Mathur, K. Bora, S. Makhija, M. Safonova, S. Agrawal

    We examine the existing metrics of habitability and classification schemes of extrasolar planets and provide an exposition of the use of computational intelligence techniques to estimate habitability and to automate the process of classification of exoplanets. Exoplanetary habitability is a challenging problem in Astroinformatics, an emerging area in computational astronomy. The paper introduces a

  • E0102-VR: Exploring the scientific potential of Virtual Reality for observational astrophysics
    Astron. Comput. (IF 1.854) Pub Date : 2019-11-14
    E. Baracaglia, F.P.A. Vogt

    Virtual Reality (VR) technology has been subject to a rapid democratization in recent years, driven in large by the entertainment industry, and epitomized by the emergence of consumer-grade, plug-and-play, room-scale VR devices. To explore the scientific potential of this technology for the field of observational astrophysics, we have created an experimental VR application: E0102-VR. The specific scientific

  • The Open Universe VOU-Blazars tool
    Astron. Comput. (IF 1.854) Pub Date : 2019-11-12
    Y.-L. Chang, C.H. Brandt, P. Giommi

    Context: Blazars are a remarkable type of Active Galactic Nuclei (AGN) that are playing an important and rapidly growing role in today’s multi-frequency and multi-messenger astrophysics. In the past several years, blazars have been discovered in relatively large numbers in radio, microwave, X-ray and γ-ray surveys, and more recently have been associated to high-energy astrophysical neutrinos and possibly

  • High Performance Computing for gravitational lens modeling: Single vs double precision on GPUs and CPUs
    Astron. Comput. (IF 1.854) Pub Date : 2019-10-31
    M. Rexroth, C. Schäfer, G. Fourestey, J.-P. Kneib

    Strong gravitational lensing is a powerful probe of cosmology and the dark matter distribution. Efficient lensing software is already a necessity to fully use its potential and the performance demands will only increase with the upcoming generation of telescopes. In this paper, we present a proof-of-concept study on the impact of High Performance Computing techniques on a performance-critical part

  • On optimising cost and value in compute systems for radio astronomy
    Astron. Comput. (IF 1.854) Pub Date : 2019-10-30
    P. Chris Broekema, Verity Allan, Rob V. van Nieuwpoort, Henri E. Bal

    Large-scale science instruments, such as the distributed radio telescope LOFAR, show that we are in an era of data-intensive scientific discovery. Such instruments rely critically on significant computing resources, both hardware and software, to do science. Considering limited science budgets, and the small fraction of these that can be dedicated to compute hardware and software, there is a strong

  • An efficient parallel semi-implicit solver for anisotropic thermal conduction in the solar corona
    Astron. Comput. (IF 1.854) Pub Date : 2019-10-23
    J. Ye, C. Shen, J. Lin, Z. Mei

    Anisotropic thermal conduction plays an important role in determining the structure of the hot plasma in the solar corona. When hot plasma appears, the conductivity rises with temperature and becomes highly nonlinear. Explicit solvers for parabolic problems often lead to much smaller time-steps limited by a Courant–Friedrichs–Lewy (CFL) condition in comparison with hyperbolic Magnetohydrodynamics (MHD)

  • Machine and Deep Learning applied to galaxy morphology - A comparative study
    Astron. Comput. (IF 1.854) Pub Date : 2019-10-21
    P.H. Barchi, R.R. de Carvalho, R.R. Rosa, R.A. Sautter, M. Soares-Santos, B.A.D. Marques, E. Clua, T.S. Gonçalves, C. de Sá-Freitas, T.C. Moura

    Morphological classification is a key piece of information to define samples of galaxies aiming to study the large-scale structure of the universe. In essence, the challenge is to build up a robust methodology to perform a reliable morphological estimate from galaxy images. Here, we investigate how to substantially improve the galaxy classification within large datasets by mimicking human classification

  • LEO-Py: Estimating likelihoods for correlated, censored, and uncertain data with given marginal distributions
    Astron. Comput. (IF 1.854) Pub Date : 2019-10-04
    R. Feldmann

    Data with uncertain, missing, censored, and correlated values are commonplace in many research fields including astronomy. Unfortunately, such data are often treated in an ad hoc way in the astronomical literature potentially resulting in inconsistent parameter estimates. Furthermore, in a realistic setting, the variables of interest or their errors may have non-normal distributions which complicates

  • On sampling of scattering phase functions
    Astron. Comput. (IF 1.854) Pub Date : 2019-09-20
    J. Zhang

    Monte Carlo radiative transfer, which has been demonstrated as a successful algorithm for modelling radiation transport through the astrophysical medium, relies on sampling of scattering phase functions. We review several classic sampling algorithms such as the tabulated method and the accept–reject method for sampling the scattering phase function. The tabulated method uses a piecewise constant approximation

  • Cleaning radio interferometric images using a spherical wavelet decomposition
    Astron. Comput. (IF 1.854) Pub Date : 2019-09-09
    C.J. Skipper, A.M.M. Scaife, J.D. McEwen

    The deconvolution, or cleaning, of radio interferometric images often involves computing model visibilities from a list of clean components, in order that the contribution from the model can be subtracted from the observed visibilities. This step is normally performed using a forward fast Fourier transform (FFT), followed by a ‘degridding’ step that interpolates over the uv plane to construct the model

  • Separating stars from quasars: Machine learning investigation using photometric data
    Astron. Comput. (IF 1.854) Pub Date : 2019-09-07
    S. Makhija, S. Saha, S. Basak, M. Das

    A problem that lends itself to the application of machine learning is classifying matched sources in the GALEX (Galaxy Evolution Explorer) and SDSS (Sloan Digital Sky Survey) catalogs into stars and quasars based on color-color plots. The problem is daunting because stars and quasars are still inextricably mixed elsewhere in the color-color plots and no clear linear/non-linear boundary separates the

  • beamModelTester: Software framework for testing radio telescope beams
    Astron. Comput. (IF 1.854) Pub Date : 2019-08-07
    O. Creaner, T.D. Carozzi

    The flux, polarimetric and spectral response of phased array radio telescopes with no moving parts such as LOFAR is known to vary considerably with orientation of the source to the receivers. Calibration models exist for this dependency such as those that are used in the LOFAR pipeline. Presented here is a system for comparing the predicted outputs from any given model with the results of an observation

  • Point source detection and false discovery rate control on CMB maps
    Astron. Comput. (IF 1.854) Pub Date : 2019-08-07
    J. Carrón Duque, A. Buzzelli, Y. Fantaye, D. Marinucci, A. Schwartzman, N. Vittorio

    We discuss the STEM (Smoothing and Testing Multiple hypotheses) procedure to search for point sources in Cosmic Microwave background maps; in particular, we aim at controlling the so-called False Discovery Rate, which is defined as the expected value of false discoveries among pixels which are labeled as contaminated by point sources. STEM is based on the following four steps: (1) needlet filtering

  • Analysing billion-objects catalogue interactively: Apache Spark for physicists
    Astron. Comput. (IF 1.854) Pub Date : 2019-07-31
    S. Plaszczynski, J. Peloton, C. Arnault, J.E. Campagne

    Apache Spark is a Big Data framework for working on large distributed datasets. Although widely used in the industry, it remains rather limited in the academic community or often restricted to software engineers. The goal of this paper is to show with practical uses-cases that the technology is mature enough to be used without excessive programming skills by astronomers or cosmologists in order to

  • DeepCMB: Lensing reconstruction of the cosmic microwave background with deep neural networks
    Astron. Comput. (IF 1.854) Pub Date : 2019-07-24
    J. Caldeira, W.L.K. Wu, B. Nord, C. Avestruz, S. Trivedi, K.T. Story

    Next-generation cosmic microwave background (CMB) experiments will have lower noise and therefore increased sensitivity, enabling improved constraints on fundamental physics parameters such as the sum of neutrino masses and the tensor-to-scalar ratio r. Achieving competitive constraints on these parameters requires high signal-to-noise extraction of the projected gravitational potential from the CMB

  • IDA software for investigating asteroid dynamics and its application to studying the motion of 2012 MF7
    Astron. Comput. (IF 1.854) Pub Date : 2019-07-15
    T.Y. Galushina, L.E. Bykova, O.N. Letner, A.P. Baturin

    In this study, we developed a new version of the Investigating Dynamics of Asteroids (“IDA”) software for investigating the dynamics and probabilistic orbital evolution of asteroids. IDA allows the motions of asteroid to be predicted in order to determine close encounters, possible collisions, and orbital resonances with planets, as well as estimating the probability of impact, visualizing the motions

  • The AIverse project: Simulating, analyzing, and describing galaxies and star clusters with artificial intelligence
    Astron. Comput. (IF 1.854) Pub Date : 2019-07-15
    K. Bekki, J. Diaz, N. Stanley

    We present our new AIverse project in which several algorithm of artificial intelligence (AI) are used to simulate, analyze, and describe the physical properties of galaxies and star clusters. The three main purposes of the project are to (i) classify the formationand evolution processes of galaxies and star clusters, (ii) perform computer simulations in an automatic way, and (iii) convert the animation

  • A machine learning classifier for microlensing in wide-field surveys
    Astron. Comput. (IF 1.854) Pub Date : 2019-07-15
    D. Godines, E. Bachelet, G. Narayan, R.A. Street

    While microlensing is very rare, occurring on average once per million stars observed, current and near-future surveys are coming online with the capability of providing photometry of almost the entire visible sky to depths up to R ∼22 mag or fainter every few days, which will contribute to the detection of black holes and exoplanets through follow-up observations of microlensing events. Based on galactic

  • StePS: A multi-GPU cosmological N-body Code for compactified simulations
    Astron. Comput. (IF 1.854) Pub Date : 2019-07-09
    G. Rácz, I. Szapudi, L. Dobos, I. Csabai, A.S. Szalay

    We present the multi-GPU realization of the StePS(Stereographically Projected Cosmological Simulations) algorithm with MPI–OpenMP–CUDA hybrid parallelization and nearly ideal scale-out to multiple compute nodes. Our new zoom-in cosmological direct N-body simulation method simulates the infinite universe with unprecedented dynamic range for a given amount of memory and, in contrast to traditional periodic

  • The varying cosmological constant models tested with Supernovae Type Ia and HII Galaxy Data
    Astron. Comput. (IF 1.854) Pub Date : 2019-06-26
    E. Dil, A.M. Oztas, E. Dil

    We have recently proposed a varying cosmological constant model for which we now test our proposal with the use of two large collections of observational Gold data of supernovae Type Ia (SNe Ia) and HII Galaxies (HIIGx) data as standard candles for constructing the Hubble diagram at redshifts beyond the current reach of Type Ia supernovae. For this aim, we obtain the luminosity distances from the proposed

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