Showing papers in "Astronomy and Computing in 2018"

TL;DR: An Artificial Neural Network Multi-Layer Perceptron (ANN MLP), Adaboost, Gradient Boosting Classifier (GBC), and XGBoost, for the separation of pulsars from radio frequency interference and other sources of noise is evaluated, using a dataset obtained from the post-processing of a pulsar search pipeline.

TL;DR: In this paper, a method for automatic detection and classification of galaxies is presented, which includes a novel data-augmentation procedure to make trained models more robust against the data taken from different instruments and contrast-stretching functions.

TL;DR: In this paper, the authors proposed a non-negative matrix factorization (NNDF) based source separation framework for multi-band images, which is based on a generalization of the NNDF to alternative and several simultaneous constraints.

TL;DR: In this paper, the authors describe the design and implementation of a GPU-based correlator and beamformer with the same capabilities as the IBM Blue Gene based systems, and show the challenges faced in selecting an appropriate system.

TL;DR: In this article, the authors proposed a new metric, the Cobb-Douglas Habitability Score (CDHS), based on the Cobb−Douglas habitability production function (CD-HPF), which computes the habitability score by using measured and estimated planetary parameters.

TL;DR: A method to smoothly remove shifts and restore sources to their reference positions, in both the catalogue and image domains, is presented, generalisable to repairing any sparsely-sampled vector field distortion to some input data.

TL;DR: This work demonstrates an approach to classifying the sources of transient RFI (in time domain data) that makes use of deep learning techniques including CNNs and LSTMs, and shows potential for development into a tool for identifying the Sources of RFI signals recorded by independent RFI monitoring stations.

TL;DR: The common mixtures-of-Gaussians density estimation approach is extended to account for situations with a known sample incompleteness by simultaneous imputation from the current model, and generalizes existing Expectation-Maximization techniques for truncated data to arbitrary truncation geometries and probabilistic rejection processes.

TL;DR: The symbolic representation of the Einstein-Boltzmann equation system in PyCosmo provides a convenient interface for implementing extended cosmological models and can be used as an independent test of their numerical solutions.

TL;DR: A major improvement to FATS is presented, which corrects inconvenient design choices, minor details, and documentation for the re-engineering process and comprises a new Python package called "feets", which is important for future code-refactoring for astronomical software tools.

TL;DR: A fast algorithm is developed based on the ideas of implicit integration of linear (Epstein) drag and exact conservation of local linear momentum for monodisperse dust-gas in two-fluid Smoothed particle hydrodynamics (SPH).

TL;DR: The public Monte Carlo photoionization and moving-mesh radiation hydrodynamics code CMacIonize is presented, which can be used to simulate the self-consistent evolution of HII regions surrounding young O and B stars, or other sources of ionizing radiation.

TL;DR: It is shown that FLaapLUC is an efficient tool to reveal transient events in Fermi -LAT data, providing quick results which can be used to promptly organise follow-up observations.

TL;DR: In this article, a GPU version of non-gridding maximum entropy method (MEM) is presented, which is tested with real and simulated data and the results show that a speedup from 1000 to 5000 times faster than a sequential version can be achieved, depending on data and image size.

TL;DR: It is found that the high time–frequency resolution of NHA and the small influence that the analysis window has on this technique facilitate effective quantitative analysis, even for LIGO signals containing mixing noise.

TL;DR: PySE as discussed by the authors is a Python software package for finding and measuring sources in radio telescope images, which can be used with images from other radio telescopes as well as to detect sources in the LOFAR telescope images.

TL;DR: The Science Portal as discussed by the authors is a web-based data interface that not only provides the framework to carry out the above steps in a systematic way, but also addresses the processing requirements by parallelizing the calculation in a transparent way for the user.

TL;DR: Variability Search Toolkit (VaST) as mentioned in this paper is a software package designed to find variable objects in a series of sky images, which can be run from a script or interactively using its graphical interface.

TL;DR: A web service framework for remote astronomical observation in Antarctica based on Python Tornado that meets the demands of real-time, multiuser remote observation and domestic users have a better experience of remote operation is introduced.

TL;DR: This paper presents the architecture and features of JOVIAL, a Cloud service where astronomers can safely use Jupyter notebooks over a personal space designed for high-performance processing under the high-availability principle and shows that features existing only in specific packages can be adapted to run in the notebooks.

TL;DR: This work presents an algorithm based on a deep convolution neural network (CNN) architecture to determine the optimal bias power for each resonator, and performs the characterization in a matter of minutes — paving the way for future mega-pixel MKID arrays.

TL;DR: This paper presents the third major release of PlanetPack that incorporates multiple improvements in comparison to the legacy versions that include modelling noise by Gaussian processes that in addition to the classic white noise may optionally include multiple components of the red noise, modulated noise, quasiperiodic noise.

TL;DR: Cross-correlated NVSS and FIRST radio catalogues having radio flux measurements at the same 1.4 GHz frequency benefit from repeated observations from both catalogues, as they give more accurate positions and fluxes and more important, reveal large differences between the two measured fluxes, thus allowing to establish radio variability.

TL;DR: This work presents a mathematical morphology approach applied to the CaII K3 series to create a tool based on the segmentation by watershed transform combined with other morphological operators to detect automatically and analyse chromospheric plages during the solar cycle 24.

TL;DR: A hybrid approach of citizen science with algorithmic scalability can facilitate big data processing for future detections as envisioned in future missions such as the Transiting Exoplanet Survey Satellite and the Wide-Field Infrared Survey Telescope (WFIRST).

TL;DR: A self-closed algorithmic pipeline aimed to process statistical samples is constructed, currently applicable to single-dimensional distributions only, but it is flexible enough to undergo further generalizations and development.

TL;DR: In this article, a robust streaming tool that leverages state-of-the-art techniques on GPU boosting, sampling, and parallel I/O is presented to significantly improve performance and scalability.

TL;DR: A global ontology (Astrophysical Services ONtology, ASON) based on web Ontology Language for Services (OWLS) to enhance existing astrophysical services description is design by expressing together VO specific and non-VO specific services design to improve the automation of services queries and allow automatic composition of heterogeneous astrophysics services.

TL;DR: In this article, the authors use Dataflow Engines (DFE) to construct an efficient Wiener filter of noisy and incomplete image data, and to quickly draw probabilistic samples of the compatible true underlying images from the Wiener posterior.