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Classification of celestial bodies and objects is the initial step in study of astronomy. Advancement in astrophotography and introduction of charge coupled devices (CCD) has made astronomical image processing easier. This study proposes classification of astronomical images into stars and galaxies based on the intensity and gradient-based features. Three machine learning algorithms, k-nearest neighbor (k-NN), support vector machine (SVM) with various kernel functions, and linear discriminant analysis (LDA) are used in this work for classification. Highest 93.39% classification accuracy has been achieved with SVM classifier and radial basis kernel function.

Spatio-temporal co-occurrence analysis captures the spatial and temporal relations between events that occur at the same time and location. In this paper, we utilize spatio-temporal co-occurrence relations to integrate solar active region (AR) data detected and reported by three feature recognition methods, namely, human labeling by forecasters at National Oceanic and Atmospheric Administration (NOAA), Spaceweather HMI Active Region Patch (SHARP) detection pipeline, and Spatial Possibilistic Clustering Algorithm (SPoCA). We determine the associations between individual reports by identifying the spatio-temporal co-occurrences among the reports from these modules. We compare our findings with the data from the Joint Science Operations Center (JSOC), analyzing the discrepancies in different circumstances. We found 105 SHARP series not properly associated with the NOAA-labeled ARs. In the end, we provide detailed movement analyses for the AR trajectories, create an updated SHARP-to-NOAA AR associations, that is crucial for space weather predictions utilizing magnetic field information, and make the ternary associations between SHARP, NOAA ARs, and SPoCA ARs available to the public.

This article presents the main results of the initial processing of some of the astronomical images obtained from the archive of the digitized photographics survey of the whole sky SuperCOSMOS. Using of the median filtering and gausian convolution procedure with the adaptive kernel allows to carry out the initial processing of astronomical images without calibration frames. After the initial processing, the photocenters of the celestial objects were identified by various methods. It has been concluded that after the use of the mathematical methods for the initial processing of the digitized photographics images, the position of objects is calculated with a higher random and systematic precision.

The Scripted E-merlin Rfi-mitigation PipelinE for iNTerferometry (SERPent) is an automated reduction and RFI-mitigation procedure utilising the SumThreshold methodology (Offringa et al. 2010b), originally developed for the LOFAR pipeline. SERPent is written in the Parseltongue language enabling interaction with the Astronomical Image Processing Software (AIPS) program. Moreover, SERPent is a simple "out of the box" Python script, which is easy to set up and is free of compilers. In addition to the flagging of RFI affected visibilities, the script also flags antenna zero-amplitude dropouts and Lovell telescope phase calibrator stationary scans inherent to the e-MERLIN system. Both the flagging and computational performances of SERPent are presented here, for e-MERLIN commissioning datasets for both L-band (1.3 - 1.8 GHz) and C-band (4 - 8 GHz) observations. RFI typically amounts to < 20 - 25% for the more problematic L-band observations and < 5% for the generally RFI quieter C-band. The level of RFI detection and flagging is more accurate and delicate than visual manual flagging, with the output immediately ready for AIPS calibration. SERPent is fully parallelised and has been tested on a range of computing systems. The current flagging rate is at 110 GB/day on a "high-end" computer (16 CPUs, 100 GB memory) which amounts to ~ 6.9 GB/CPU/day, with an expected increase in performance when e-MERLIN has completed its commissioning. The refining of automated reduction and calibration procedures is essential for the e-MERLIN legacy projects and future interferometers such as the SKA and the associated pathfinders (MeerKAT and ASKAP), where the vast data sizes (> TB) make traditional astronomer interactions unfeasible., 15 pages, 13 figures, Astronomy and Computing, Volume 2, p. 54-66

Big Data problems dealing with a variety of large data sets are now common in a wide range of domain science research areas such as bioinformatics, social science, astronomical image processing, weather and climate dynamics, and economics. In some cases, the data generation and computation is done on high performance computing (HPC) resources, thus presenting an incentive for developing/optimizing big data middleware and tools to take advantage of the existing HPC infrastructures. Data-intensive computing middleware (such as Hadoop, Spark) can potentially benefit greatly from the hardware already designed for high performance and scalability with advanced processor technology, large memory/core, and storage/filesystems. SDSC Comet represents such a resource with large numbers of compute nodes with fast node local SSD storage, and high performance Lustre filesystems. This paper discusses experiences and benefits of using optimized Remote Direct Memory Access (RDMA) Hadoop and Spark middleware on the XSEDE Comet HPC resource, including some performance results of Big Data benchmarks and applications. Comet is a general purpose HPC resource so some work is needed to integrate the middleware to run within the HPC scheduling framework. This aspect of the implementation is also discussed in detail.

A project at Harvard University is proving that two very different disciplines have very much in common. The astronomical medicine project (http://astromed.iic.harvard.edu/) is working to convert medical imaging software into tools that fuel discoveries in astronomy. But if the scientists behind the project have their way, any discipline that relies on large, complex data sets will reap the benefits.

Written in Python and utilising ParselTongue to interface with the Astronomical Image Processing System (AIPS), the e-MERLIN data reduction pipeline is intended to automate the procedures required in processing and calibrating radio astronomy data from the e-MERLIN correlator. Driven by a plain text file of input parameters, the pipeline is modular and can be run in stages by the user, depending on requirements. The software includes options to load raw data, average in time and/or frequency, flag known sources of interference, flag more comprehensively with SERPent, carry out some or all of the calibration procedures including self-calibration), and image in either normal or wide-field mode. It also optionally produces a number of useful diagnostic plots at various stages so that the quality of the data can be assessed. The software is available for download from the e-MERLIN website or via Github., Comment: 6 pages, 1 figure. Code DOI: 10.5281/zenodo.10163 [ascl:1407.017]

The image processing is becoming a key issue in astronomical data analysis. This paper introduces the algorithms and methods applied in astronomical image processing from different aspects. The paper first shows methods of lossless compression of astronomical images, like the pyramidal median transform, segment-based DPCM and 5/3 integer wavelet transform. Secondly it shows the algorithms of astronomical image segmentation, edge detection and de-noising. Finally, many different methods of image recovery and restoration are introduced briefly. We summarize a number of recent achievements on astronomical image processing in this survey, and list the recent published papers.

International audience; In this paper a novel approach of stereoscopic depth inpainting is used to model lunar craters. In the case of lunar mission, the disparity map generation using stereoscopic images is limited by shadows inside impact craters. Impact craters are most important features on lunar surface but cannot be modeled using stereo cue alone. Taking into consideration the importance of crater modeling and limitations of stereo cue, we are proposing stereoscopic depth inpainting method as an additional method to model the lunar impact crater.

When astronomers analyze sky images, they need to identify the newly observed celestial objects in the catalog of known objects. We have developed a technique for indexing catalogs, which supports fast retrieval of closely matching catalog objects for every object in new images. It allows processing of a sky image in less than a second, and it scales to catalogs with billions of objects.

The new concept of reprogrammable digital circuit implementation for auto-guiding system is shown in this paper. It is dedicated to astrophotography and operates with popular CCD webcam and ST4 compatible equatorial mounts. This idea was verified by MATLAB model, which was used to test all procedures and data conversions. Next the circuit prototype was implemented at Altera MAX II CPLD device and tested. The project was design also for amateur-astronomy so costs, user friendly configuration and maintenance are very important factors which were taken into consideration

Columnist Charles Day describes how the Internet boosts the beauty and power of astronomical images compared to seeing those same images on paper.

With the requirements of scientific and medical image database support in mind, we describe a range of useful technologies for storage, transmission and display. These new technologies are all based on discrete wavelet or related multiscale transforms. Other important issues include noise modeling, and the innovative use of entropy for information characterization.

All raw data recorded by the Very Large Array (VLA) since it began operation in 1980 have been archived, test observations and calibrations as well as program observations. The reels of tape are kept in a vault in the control building of the VLA on the Plains of San Augustin in New Mexico; the vault was filled several years ago and so recordings for recent years are in a trailer parked outside the control building. The data are in the public domain, by policy, with a proprietary period of 18 months for program observations. A subset of the observation catalog (all non-calibrator observations with duration longer than one hour) is distributed as a set of floppy disks (MS-DOS format) with a search program (for MS-DOS); the full catalog is available on a computer at the Array Operations Center in Socorro, New Mexico. One technical problem is that the existing catalog doesn’t tabulate which reel of tape in the vault has which day’s data. Reel numbers must be obtained from a log book at the VLA site. But the data tapes can be located in the vault, they are coded in a modest set of reasonably self-documenting formats, and a portable program which is exported by the National Radio Astronomy Observatory (NRAO) in all AIPS (Astronomical Image Processing System) kits can be used to read files copied from them.

Astronomical Image Processing (AIP) needs extremely interactive software systems. It is therefore necessary to execute in a short time frequently used, time consuming elaborations, such as convolution, geometrical normalization and geometrical correction. In this paper such operations have been splitted in a scalar part (typical of the operation) and in a vectorial part (in common among the whole operation set). In this way it is possible to achieve their optimization with regard to a native or attached vector processor. The common vectorial part (vectorial nucleus) is studied in detail and checked on VAX 11/780+FPS 5410, CRAY X-MP/12 and IBM 3090VF processors.

This reply is prompted by Fred Maryanski's question in his Chairman's column (Sigsmall Newsletter, August 1982). "What will we do with all that bandwidth?" This response follows my consideration of the implications for on-line video signal processing given the advent of 2Ø MHz bus, 8 Mbyte, 32-bit minicomputer systems. This is what "think Mini" now means for us in astronomical image processing applications. We are now considering the question: What happens in a modestly networked video signal processing environment?

The topics dealt with in this paper are as follows. Firstly, to set the scene, we briefly overview the major astronomical image processing systems, and make reference to current software engineering problems in this area. Secondly, we survey a range of pattern recognition problems, which all have classification as their central objective. These pattern recognition problems are: object searching and classification in photometry; the classifying of galaxies on the basis of their morphological shapes; and the classification of stellar spectra. Finally, we review some of the specific problems expected for Hubble Space Telescope image data.

Multiresolution transforms have been used with great effectiveness in many areas of astronomical image processing. Here we seek to extend their applicability in two directions. Firstly, we use such transforms to detect objects in fields containing point sources, or a mixture of point sources and large, extended objects. Secondly, we discuss the efficient transfer of (compressed) images using current World-Wide Web technology.

We discuss a collection of programs to display and analyze astronomical images on IBM-PC and compatible computers. These programs, based on the VISTA program at Lick Observatory and on DAOPHOT at Dominion Astrophysical Observatory, have facilities to display data in FITS format on a variety of monitors, to perform elementary arithmetic operations on images, to do simple photometry, and to extract one-dimensional information. The merits of PC versus minicomputer-based image processing are discussed.