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5 articles
Editorial
Computational Intelligence in Astronomy
Ping Guo, Yuping Wang, Hailin Liu, Yiu-ming Cheung
Pages: 573 - 574
Review Article
Computational Intelligence in Astronomy: A Survey
Ke Wang, Ping Guo, Fusheng Yu, Lingzi Duan, Yuping Wang, Hui Du
Pages: 575 - 590
With explosive growth of the astronomical data, astronomy has become a representative data-rich discipline so as to defy traditional research methodologies and paradigm to analyze data and discover new knowledge from the data. How to effectively process and analyze the astronomical data is a fundamental...
Research Article
Heterogeneous Information Network Embedding based Personalized Query-Focused Astronomy Reference Paper Recommendation
Xiaoyan Cai, Junwei Han, Shirui Pan, Libin Yang
Pages: 591 - 599
Fast-growing scientific papers bring the problem of rapidly and accurately finding a list of reference papers for a given manuscript. Reference paper recommendation is an essential technology to overcome this obstacle. In this paper, we study the problem of personalized query-focused astronomy reference...
Research Article
An Optimal Task-Scheduling Strategy for Large-Scale Astronomical Workloads using In-transit Computation Model
Xiaoli Wang, Bharadwaj Veeravalli, Omer F. Rana
Pages: 600 - 607
The Sloan Digital Sky Survey (SDSS) has been one of the most successful sky surveys in the history of astronomy. To map the universe, SDSS uses their telescopes to take pictures of the sky over the whole survey area. Now the total SDSS data volume is larger than 125 TB since every night telescopes produce...
Research Article
Machine Learning for Stellar Magnetic Field Determination
J.P. Córdova Barbosa, S.G. Navarro Jiménez, J.C. Ramírez Vélez
Pages: 608 - 615
Abstract
In this work we present the results for the automatic determination of the mean longitudinal magnetic field in polarized stellar spectra through the analysis of spectropolarimetric observations. In order to determine this important parameter, we first developed a synthetic database encompassing...