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2025 International conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Mohite, Vaishnavi Shivaji Patra, Krishna Gayatri Sankar, C.H. Hari Jayan, Sarada Amrita School of Computing Department of Computer Science Bengaluru India Amrita School of Engineering Department of Mathematics Bengaluru India

ISBN: (纸本)9798331538538

By analyzing vast amounts of observational data through astronomical image analysis, you can gain valuable information about the structure of the universe. This paper presents a new method for clustering astronomical images that uses both spectral and K-means methods and also incorporates linear algebra and optimization principles. We propose a method that combines feature extraction techniques such as the Fourier transform with clustering algorithms to classify celestial objects based on their characteristics. The research examines the effectiveness of spectral clustering and K-means clustering in organizing astronomical images into meaningful groups, taking into account factors such as computer efficiency, clustering accuracy and interpretability. Experimenting with different datasets, including telescope images, we demonstrate the effectiveness of our approach in automating the classification of astronomical objects. Our results reveal insights into the underlying structure of astronomical knowledge and offer promising avenues for future research in computational astronomy. In general, this research advances both the field of astronomy and the application of mathematical methods to image analysis and clustering. © 2025 IEEE.

关键词： K-means clustering

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High Performance Stingray: fast spectral timing for all

High Performance Stingray: fast spectral timing for all

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Euromicro conference on Parallel, Distributed and Network-Based Processing

作者： Matteo Bachetti Eleonora Veronica Lai Daniela Huppenkothen Guglielmo Mastroserio Matteo Lucchini Osservatorio Astronomico di Cagliari Istituto Nazionale di Astrofisica Cagliari Italy Anton Pannekoek Institute for Astronomy University of Amsterdam Amsterdam the Netherlands Dipartimento di Fisica Università degli Studi di Milano Milano Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Celestial objects are known to be change in brightness over time, driven by a diverse combination of physical processes, whose time scales range from sub-milliseconds to billions of years. Stingray is an open-source Python package that brings advanced time series analysis techniques to the astronomical community, with a focus on high-energy astrophysics, but built on top of general-purpose classes and methods that are designed to be easily adapted and extended to other use cases. We describe the work being done to adapt Stingray to the analysis of large data archives. In particular, we measure the performance and scalability of Stingray and use parallel computing to speed up selected parts of the code.

关键词： Codes Atmospheric measurements Astrophysics Scalability Time series analysis Brightness Parallel processing Particle measurements Extraterrestrial measurements Timing

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Spoke 3 Big data Management, Storage, and Archive Infrastructure 33

Spoke 3 Big Data Management, Storage, and Archive Infrastruc...

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33rd Euromicro International conference on Parallel, Distributed, and Network-Based Processing, PDP 2025

作者： Coran, Giacomo Costantini, Massimo Gelsumini, Sara Busonero, Deborah Knapic, Cristina Adelfio, Andrea Ciangottini, Diego INAF - OATs Trieste Italy INAF - OATo Turin Italy INFN Sezione di Perugia Perugia Italy

ISBN: (纸本)9798331524937

The project aims to develop innovative data management solutions (database and file system archive) for the Spoke 3 division of the Italian National Center for HPC, Big data, and Quantum Computing, rigorously applying the FAIR principles and Open Science methodologies. This system provides a robust and scalable framework designed to address big data challenges in astrophysics, which requires customized data models and management solutions. It focuses on managing observations from the Gaia and Fermi space missions, as well as simulations generated using the Pluto and Ramses codes, with the goal of supporting requests from the astrophysical Italian community on particular scientific *** Gaia data, we identified the HDF5 as a suitable format to improve data access and analysis outside the data reduction pipelines. The HDF5 format allows the nesting of primitive fields, arrays, and complex objects, thereby enhancing accessibility and computational efficiency. For the data management of the FT1 and FT2 data products from the Fermi Observatory, a common data model has been chosen to provide a unique point of access to the scientific data, employing logic for database queries and offering access to data products over a long observing period. Finally, to facilitate user interaction, a custom web application has been developed, providing a user-friendly interface for accessing and querying the archived metadata, additional 'Cut & Merge' and 'Transits' services, and supporting also secure authentication methods to ensure data privacy and compliance. These comprehensive resources enhance data accessibility and usability through the Spoke3 Archive Infrastructure, empowering the scientific community to explore and analyze astronomical data more effectively. © 2025 IEEE.

关键词： Information management

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Spoke 3 Big data Management, Storage, and Archive Infrastructure

Spoke 3 Big Data Management, Storage, and Archive Infrastruc...

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Euromicro conference on Parallel, Distributed and Network-Based Processing

作者： Giacomo Coran Massimo Costantini Sara Gelsumini Deborah Busonero Cristina Knapic Andrea Adelfio Diego Ciangottini INAF - OATs Trieste Italy INAF - OATo Turin Italy INFN Sezione di Perugia Perugia Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The project aims to develop innovative data management solutions (database and file system archive) for the Spoke 3 division of the Italian National Center for HPC, Big data, and Quantum Computing, rigorously applying the FAIR principles and Open Science methodologies. This system provides a robust and scalable framework designed to address big data challenges in astrophysics, which requires customized data models and management solutions. It focuses on managing observations from the Gaia and Fermi space missions, as well as simulations generated using the Pluto and Ramses codes, with the goal of supporting requests from the astrophysical Italian community on particular scientific *** Gaia data, we identified the HDF5 as a suitable format to improve data access and analysis outside the data reduction pipelines. The HDF5 format allows the nesting of primitive fields, arrays, and complex objects, thereby enhancing accessibility and computational efficiency. For the data management of the FT1 and FT2 data products from the Fermi Observatory, a common data model has been chosen to provide a unique point of access to the scientific data, employing logic for database queries and offering access to data products over a long observing period. Finally, to facilitate user interaction, a custom web application has been developed, providing a user-friendly interface for accessing and querying the archived metadata, additional “Cut & Merge” and “Transits” services, and supporting also secure authentication methods to ensure data privacy and compliance. These comprehensive resources enhance data accessibility and usability through the Spoke3 Archive Infrastructure, empowering the scientific community to explore and analyze astronomical data more effectively.

关键词： Quantum computing Observatories databases Space missions Pipelines Restful API Big data Metadata Usability Pluto

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NUMERICAL-SIMULATION OF astronomical OBSERVATIONS WITH ISO-LWS

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NUOVO CIMENTO DELLA SOCIETA ITALIANA DI FISICA C-GEOPHYSICS AND SPACE PHYSICS 1993年第5期16卷 605-611页

作者： TOMMASI, E SARACENO, P ORFEI, R IFSI-CNR Frascati (Roma)

The purpose of this work is the numerical simulation of the ISO-LWS spectrometer output with a computer code aiming at obtaining data similar as far as possible to those to be gained during the mission. With such data the processing and analysis programmes will be tested in order to assess the instrument capabilities and limits. As an example we have simulated the realization of a spectral and spatial map of the sky region with the outflow of L1551-IRS5.

关键词： astronomical and space-research instrumentation galactic an extragalactic objects and systems Other topics on stellar systems Stellar characteristics and properties

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Compression of astronomical images based on the Karhunen-Loeve Transform

Compression of astronomical images based on the Karhunen-Loe...

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8th IASTED International conference on Signal and Image Processing

作者： Pata, Petr Czech Tech Univ Dept Radioelectr Prague 16627 Czech Republic

ISBN: (纸本)9780889865839

The study deals with possibility of the Karhunen - Loeve Transform (KLT), also known as PCA (Principal Component analysis), for astronomical data compression. This approach is based on analysis of statistical properties of these kinds of data. Image data are divided into four groups - dark frame (DF), flat field (FF), light image (LI) and deep sky light image (DSLI). Quality measurement of the influence of loss part of the coder has been based on standard signal functions (mean square error - MSE, peak signal to noise ratio PSNR), subjective quality measurement according to ITU standard (International Telecommunication Union) and the efficiency astronomical image processing. The astronomical (scientific) image processing include star position measurement (astrometry), brightness measurement (photometry), estimation of deformation the point spread function of the detection (PSF) and etc. The total amount of 20% KLT spectral components has been determined as a sufficient number for high precision measurement. The course of covariance matrix eigenvalues (and eigenimages also) analysis has been carried out. Real images from BOOTES experiment (Burst Observer of Optical Transient Exploring System) have been used as testing signals.

关键词： astronomical image data compression Karhunen - Loeve Transform image quality measurement BOOTES project

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StarWatch Radio astronomical Monitoring in Virtual Environment

StarWatch Radio Astronomical Monitoring in Virtual Environme...

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International conference on Cyberworlds CW

作者： Klimenko, Stanislav Malofeev, Valery Nikitin, Igor Inst Comp Phys & Technol Protvino Russia Pushchino Radio Astron Observ Pushchino Russia Fraunhofer Inst Algorithms & Sci Comp St Augustin Germany

ISBN: (纸本)9781467394031

An application of Virtual Environment for real-time analysis of radio astronomical data is presented. Serving as an interface to radio astronomical databases or being applied to live data from the radio telescope, the application supports various data filters measuring signal-to-noise ratio (SNR), Doppler's drift, degree of signal localization on celestial sphere and other useful tools for signal extraction and classification. The application is created using Avango VE framework (avango. com). The application has been used for analysis of radio astronomical data from SETI Institute (setilive. org).

关键词： Big data processing radio astronomy scientific visualization

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Comparison of algorithms for the efficient approximation of heterogeneous multidimensional scientific data

Comparison of algorithms for the efficient approximation of ...

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conference on astronomical data analysis II

作者： Freedman, I Dr Immanuel Freedman Inc La Jolla CA 92037 USA

ISBN: (纸本)0819446262

Many scientists would like to be able to view and analyze quick look astronomical data on hand held devices linked by wireless network to the Internet. Scientific data is often characterized by high dynamic range together with abrupt, localized or extended changes of spatial and temporal statistical properties. I compare the effectiveness of algorithms for the efficient approximation of scientific data that support low bit-rate, near real-time and low-delay communication of heterogeneous multidimensional scientific data over existing or planned wireless networks.

关键词： data analysis image processing astronomical data bases : miscellaneous

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Extraction of catalogs from astronomical images

Extraction of catalogs from astronomical images

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conference on astronomical data analysis

作者： Tagliaferri, R Longo, G Iovane, G Osserv Astron Capodimonte I-80131 Naples Italy

ISBN: (纸本)0819441910

The advent of large format CCD detectors is producing a data flow which cannot be handled with traditional interactive software tools and to be effectively exploited need automatic tools for catalogue extraction and analysis. NExt (Neural Extractor) is a neural network based package capable to perform in a fully automatic way both object detection and star/galaxy classification on large format astronomical images. In this paper we shortly summarize the main aspects of the package stressing some innovative aspect of the procedure implemented to perform the automatic extraction of the data set to be used for the training.

关键词： neural networks catalog extraction image segmentation star-galaxy classification

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GAIDR: An Efficient Time Series Subsets Retrieval Method for Geo-Distributed astronomical data 20

GAIDR: An Efficient Time Series Subsets Retrieval Method for...

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20th IEEE International conference on High Performance Computing and Communications (HPCC) / 16th IEEE International conference on Smart City (SmartCity) / 4th IEEE International conference on data Science and Systems (DSS)

作者： Hu, Xiaoteng Yu, Ce Li, Bingyao Tang, Shanjiang Xiao, Jian Huang, Yanyan Tianjin Univ Sch Comp Sci & Technol Tianjin 300350 Peoples R China Hebei Univ Technol Serv Ctr Informat Secur & Technol Tianjin 300130 Peoples R China

ISBN: (纸本)9781538666142

Time series subsets retrieval is an important problem in astronomical image data analysis, and the original astronomical files are usually stored in geo-distributed data centers. Current data retrieval approach consists of three manual steps, i.e., retrieving original astronomical files, ordering filtered files and obtaining target sub-files. Besides, astronomers need to download or copy entire datasets to their local machines from different data centers before the first step. The retrieval method can only work for a small amount of data. However, astronomical images are big data, indicating that it is time-consuming to process the massive geo-distributed data and requires a significant amount of manual work. In this paper, we propose Geo-Distributed Astronomy Image data Retrieval (GAIDR), an automatic and efficient subsets retrieval method for astronomical time series data in a geo-distributed environment, and implement a data retrieval system based on it. The retrieval system automatically inquires and returns the target astronomical sub-files by using indexes of astronomical files, including original files index and replica index. Moreover, to further accelerate the retrieval of astronomical time series subsets, a replica strategy is designed in GAIDR method. The replica strategy can be utilized to reduce the size of replica files and merge the related files via replica coordinate-mapping algorithm. It recognizes the hot replica files on the basis of history requests and optimizes replica files data layout according to the temporal and spatial characteristic of data access in time domain astronomy research. Experimental results show that GAIDR method can achieve the highest replica hit ratio among all test methods, and the average response time can be reduced by at least 14.07% comparing with other methods.

关键词： FITS file geo-distributed replica strategy retrieval method time series subsets

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