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检索条件"任意字段=Conference on Astronomical Data Analysis"

共 1175 条记录，以下是1-10 订阅

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astronomical data analysis Software and Systems XVII: Proceedings of a conference Held in Kensington Town Hall, London, United Kingdom 23-26 September ... Society of the Pacific conference)

Astronomical Data Analysis Software and Systems XVII: Procee...

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2008年

作者： Robert W Argyle Peter S Bunclark James R Lewis

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Jovial: astronomical data analysis Cloud Service 1

Jovial: Astronomical Data Analysis Cloud Service

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1st International conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Mohammed, Sufiyan Ahmed Indiana Wesleyan University Indiana United States

ISBN: (纸本)9798350355611

This paper presents 'Jovial,' an innovative cloud service designed for astronomers to efficiently analyze astronomical data from the Virtual Observatory. Leveraging Docker containers, Kubernetes orchestration, and high-performance file systems, Jovial provides a secure and high-availability platform for users to run code, visualize data, and manage files within a controlled environment. This infrastructure ensures the protection of resources and user data, making it a valuable tool for the astronomical community. © 2025 IEEE.

关键词： Cloud platforms

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astronomical Image Processing by the Lemur Software 24

Astronomical Image Processing by the Lemur Software

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6th Asia conference on Machine Learning and Computing, ACMLC 2024

作者： Khlamov, Sergii Savanevych, Vadym Briukhovetskyi, Oleksandr Vlasenko, Vladimir Dikov, Yevhen Dmytrenko, Artem Department of Media Systems and Technologies Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of systems engineering Kharkiv National University of Radio Electronics Kharkiv Ukraine Western Center of Radio Engineering Surveillance National Space Facilities Control and Test Center Kyiv Ukraine Space Research and Communication Center National Space Facilities Control and Test Center Kyiv Ukraine Department of Software Engineering GlobalLogic Incorporation Kharkiv Ukraine Institute of Astronomy V. N. Karazin Kharkiv National University Kharkiv Ukraine

ISBN: (纸本)9798400710018

The astronomical images are made by cameras with the charge-coupled device (CCD). They can be received from the different sources, like servers, clusters, predefined series, archives or "live" (online) data streams. The astronomical images processing is focused on but not limited to the following tasks: data mining, knowledge discovery, big astronomical data processing, filtering, background alignment, brightness equalization, segmentation, classification, image recognition, object's image detection, object's astrometry and photometry, moving object detection, parameters determination of the object's image and apparent motion, reference objects selection and others. The modern Lemur software of the Collection Light Technology (CoLiTec) project (https://***) was developed using the described above technologies and approaches. The Lemur software is designed to perform a sequence of the following main steps: pre-processing (astronomical information collection -> worst data rejection -> useful data extraction -> data mining -> classification -> background alignment -> brightness equalization), image processing (segmentation -> typical form analysis -> recognition patterns applying -> detection of the object's image -> astrometry -> photometry -> objects identification -> tracks detection), knowledge discovery (Solar System objects or artificial satellites to be discovered, tracks parameters for the investigation, light curves of the variable stars, scientific reports in the international formats). The paper describes the modern features for the astronomical image processing implemented in the Lemur software. It has assisted in making over 1700 discoveries of asteroids, including 5 NEOs, 21 Trojan asteroids of Jupiter, 1 Centaur. In total it has been used in about 800 000 observations, during which five comets were discovered. All such observations and discoveries have official observational circulares from the Minor Planet Center (MPC) of the International Ast

关键词： Asteroids

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14th Arabic conference of the Arab Union for Astronomy and Space Sciences, AUASS-CONF 2023

14th Arabic Conference of the Arab Union for Astronomy and S...

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14th Arabic conference of the Arab Union for Astronomy and Space Sciences, AUASS-CONF 2023

ISBN: (纸本)9789819632756

The proceedings contain 21 papers. The special focus in this conference is on Arab Union for Astronomy and Space Sciences. The topics include: Foreword;Transiting Exoplanets from Sharjah astronomical Observatory (SAO-M47): The Exoplanet HAT-P-25 b Using L & V Filters;the Jewel in the Crown: Archiving and Analyzing astronomical Spectro-Visual Binaries Big data;new Insight Concerning Primordial Lithium Production;Neutrinos from Type-II Supernovae;estimation of the Transit Time of Multiple Coronal Mass Ejection Events;A Comprehensive Study of the Physical and Geometrical Characteristics of the Close Visual Binary System HIP 45571;The United Nations Group of Experts on the Standardization of Geographical Names (UNGEGN) and Its Relationship with the International astronomical Union (IAU) on the Naming/Designation of Extraterrestrial Objects’;Assessment of Active Space Debris Removal Methods Using the Weighted Sum Model (WSM);thermal Control and analysis of a 3U Nanosatellite with Deployed Panels;advancements in Astronomy and Space Sciences in Jordan: Contributions from Experts and Astrophysical Institutions;the Central Distance Distribution of Hα and γ-ray Burst Solar Flares;update of the Solar Radiation Atlas for the Arab Republic of Egypt 2023;jurisprudential Reliance on astronomical Calculations in Determining the Beginnings of the Hijri month;sun Vertical Depressions and Their Effects on the Morning Twilight Phases in Egypt;thermal analysis of an Educational Nano Satellite;Twenty-Five years of the Foundation of the Arab Union for Astronomy and Space Sciences (AUASS);legal Challenges in Establishing Human Settlements in Space;a City-Wide Planetarium;controlling Robot Through Satellite: Telesurgery or Remote Surgery.

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RFI mitigation algorithm based on adaptive filter 3

RFI mitigation algorithm based on adaptive filter

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3rd International conference on Electrical, Electronics, and Information Engineering, EEIE 2024

作者： Li, Jian Ergesh, Toktonur Chen, Maozheng Duan, Xuefeng Xinjiang Astronomical Observatory Chinese Academy of Sciences Urumqi830011 China University of Chinese Academy of Sciences Beijing100049 China Xinjiang Key Laboratory of Microwave Technology Urumqi830011 China Jihua Laboratory Foshan528000 China

ISBN: (纸本)9781510688179

Aiming at the problem of radio observation data quality degradation or invalid data caused by Radio Frequency Interference (RFI), we have studied the adaptive RFI algorithm and proposed an interference mitigation method based on the adaptive filter. An adaptive interference suppression model has been established to effectively counter complex and intense interference. This model includes a normalized factor, derived from the filter's correlation function, which improves the performance of the adaptive filtering algorithm in reducing RFI. The adaptive filter algorithm flexibly adjusts the filter gain automatically, based on the correlation between the RFI reference signal and the observation signal, thereby effectively eliminating the RFI mixed with astronomical signals. Simulation results show that the algorithm can effectively eliminate radio frequency interference, improve the signal-to-noise ratio of astronomical signals and signal recognition rate. It also prevents the loss of signal bandwidth, ensuring the provision of reliable data for subsequent post-processing analysis. © The Authors.

关键词： Adaptive filtering

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Exoplanet discovery using multi-method observational data & machine learning techniques 1st

Exoplanet discovery using multi-method observational data & ...

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1st International conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Kaushik, Mahima Mattoo, Aditee Rastogi, Ritesh Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Department of IT Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032874036

The discovery of exoplanets in recent years has changed the field of exoplanets. Scientists continue to work to explore and discover more of these new resources for life. Machine learning plays an important role in the detection, classification and study of exoplanets. This paper explores the potential of various machine learning and data analysis techniques to process large astronomical datasets from space missions for exoplanet detection. ExoMiner achieved the best results comparing studies, achieving 99% sensitivity and 93% accuracy. Other machine learning models and bit-shuffle pre-processing algorithms with performance up to 3.2 GB/s have also contributed to the search and classification of extrasolar planets. These machine learning algorithms include Box Least Square (BLS), wavelet, cross- correlation function (CCF), convolutional neural network (CNN), support vector machine (SVM), etc. data processing techniques such as data normalization, data integration, and CoLiTec software are also discussed. © 2025 the Author(s).

关键词： Convolutional neural networks

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Jovial: astronomical data analysis Cloud Service

Jovial: Astronomical Data Analysis Cloud Service

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AIML-Applications for Engineering & Technology (ICAET), International conference on

作者： Sufiyan Ahmed Mohammed Indiana Wesleyan University Indiana United States

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

This paper presents “Jovial,” an innovative cloud service designed for astronomers to efficiently analyze astronomical data from the Virtual Observatory. Leveraging Docker containers, Kubernetes orchestration, and high-performance file systems, Jovial provides a secure and high-availability platform for users to run code, visualize data, and manage files within a controlled environment. This infrastructure ensures the protection of resources and user data, making it a valuable tool for the astronomical community.

关键词： Cloud computing Observatories data analysis High performance computing Scalability Collaboration Computer architecture Stability analysis Real-time systems Thermal stability

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Capitalizing the Predictive Potential of Machine Learning to Detect Various Stratigraphic Units of Lunar Craters Using Multi-Source Lunar Exploration data 24

Capitalizing the Predictive Potential of Machine Learning to...

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8th International conference on Advances in Artificial Intelligence, ICAAI 2024

作者： Zuo, Wei Gao, Xingye Xiong, Yaying Wang, Fang Li, Chunlai National Astronomical Observatories Chinese Academy of Sciences Beijing China School of Astronomy and Space Science University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400718014

This study utilized multi-class lunar exploration data including imagery and topographic data from Chang’e-2, USGS Geological Map, LROC WAC, and Kaguya MI to classify different geological unit types of lunar craters through machine learning. Focusing on the landing area of Chang’e-5 as the study region, various features such as morphology and mineral composition of craters were extracted to establish a predictive model using five machine learning algorithms including XGBoost. Results indicate that XGBoost exhibited the highest overall predictive accuracy with a classification accuracy of 97.7%, and a weighted F1 score of 97.6%. This study confirms the effectiveness of feature selection in enhancing model classification accuracy, revealing significant correlations between elevation, FeO content features, and the geological units where craters are located within the study area. The research achieved precise prediction of geological unit types within lunar craters. The predictive outcomes not only estimate the geological age represented by craters but also provide insights and references for future research on establishing quantitative analysis models for crater stratigraphic age based on morphological parameters and rock abundance. © 2024 Copyright held by the owner/author(s).

关键词： Stratigraphy

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Investigation variable star classification through light curve analysis using machine learning approach

Investigation variable star classification through light cur...

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2024 International conference on Photonics Solutions, ICPS 2024

作者： Tongleak, Chutipon Thongsuwan, Setthanun Srithongtae, Kewalee Kitrattana, Borirak Tanirat, Purin Channumsin, Sittiporn Buranasiri, Prathan Artificial Intelligence Photonics Advanced Research Laboratory Department of Physics School of Science King Mongkut's Institute of Technology Thailand Electronic and Optoelectronic Device Research Unit School of Science King Mongkut's Institute of Technology Ladkrabang Thailand Department of Information Technology Sriracha Faculty of Science Kasetsart University Sriracha Campus Thailand Thailand

ISBN: (数字)9781510688308

ISBN: (纸本)9781510688292

With the development of space technology, wide-field sky surveys using telescopes have expanded the range of new data available for time-domain astronomical research. Traditional data analysis methods can no longer respond quickly and accurately enough to the growing volume of data. Thus, classifying time-series data, such as light curves, has become a significant challenge in the era of big data. In modern times, analyzing light curves has become essential for using machine learning techniques to handle and filter through massive amounts of data. Machine learning algorithms can be divided into two categories: shallow learning and deep learning. Numerous researchers have proposed and developed a variety of algorithms for light curve classification. In this study, we experimented with Support Vector Machine (SVM) and XGBoost, which are shallow machine learning algorithms, as well as 1D-CNN and Long Short-Term Memory (LSTM), which are deep learning algorithms, which are branches of deep machine learning, to classify variable stars. The training and testing data used in this study were from the Optical Gravitational Lensing Experiment-III (OGLE-III), consisting of variable star data from the Large Magellanic Cloud (LMC), categorized into five main classes: Classical Cepheids, δ Scutis, eclipsing binaries, RR Lyrae stars, and Long-period variables. The results demonstrate the performance analysis of each machine learning algorithm type applied to light curve data, while also highlighting the accuracy and statistical metrics of the algorithms used in the experiments. © 2025 SPIE.

关键词： Contrastive Learning

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astronomical Image Clustering Using Spectral, K-Means and Mean Shift

Astronomical Image Clustering Using Spectral, K-Means and Me...

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

作者： Vaishnavi Shivaji Mohite Krishna Gayatri Patra CH Hari Sankar Sarada Jayan Department of Computer Science Amrita School of Computing Bengaluru Department of Mathematics Amrita School of Engineering Bengaluru

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

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.

关键词： Image analysis Fourier transforms Accuracy Heuristic algorithms Clustering algorithms Linear algebra Feature extraction Optimization Astronomy Tuning

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