The application of data mining in astronomical surveys,such as the Large Sky Area MultiObject Fiber Spectroscopic Telescope(LAMOST)survey,provides an effective approach to automatically analyze a large amount of compl...The application of data mining in astronomical surveys,such as the Large Sky Area MultiObject Fiber Spectroscopic Telescope(LAMOST)survey,provides an effective approach to automatically analyze a large amount of complex survey data.Unsupervised clustering could help astronomers find the associations and outliers in a big data set.In this paper,we employ the k-means method to perform clustering for the line index of LAMOST spectra with the powerful software Astro Stat.Implementing the line index approach for analyzing astronomical spectra is an effective way to extract spectral features for low resolution spectra,which can represent the main spectral characteristics of stars.A total of 144 340 line indices for A type stars is analyzed through calculating their intra and inter distances between pairs of stars.For intra distance,we use the definition of Mahalanobis distance to explore the degree of clustering for each class,while for outlier detection,we define a local outlier factor for each spectrum.Astro Stat furnishes a set of visualization tools for illustrating the analysis results.Checking the spectra detected as outliers,we find that most of them are problematic data and only a few correspond to rare astronomical objects.We show two examples of these outliers,a spectrum with abnormal continuum and a spectrum with emission lines.Our work demonstrates that line index clustering is a good method for examining data quality and identifying rare objects.展开更多
基金supported by the Joint Research Fund in Astronomy (U1631239) under cooperative agreement between the National Natural Science Foundation of China (NSFC) and Chinese Academy of Sciences (CAS)supported by the International Science and Technology Cooperation Program of China (2014DFE10030)the Basic Science and Engineering Special Project of Heilongjiang Province Education Department (135109219)
文摘The application of data mining in astronomical surveys,such as the Large Sky Area MultiObject Fiber Spectroscopic Telescope(LAMOST)survey,provides an effective approach to automatically analyze a large amount of complex survey data.Unsupervised clustering could help astronomers find the associations and outliers in a big data set.In this paper,we employ the k-means method to perform clustering for the line index of LAMOST spectra with the powerful software Astro Stat.Implementing the line index approach for analyzing astronomical spectra is an effective way to extract spectral features for low resolution spectra,which can represent the main spectral characteristics of stars.A total of 144 340 line indices for A type stars is analyzed through calculating their intra and inter distances between pairs of stars.For intra distance,we use the definition of Mahalanobis distance to explore the degree of clustering for each class,while for outlier detection,we define a local outlier factor for each spectrum.Astro Stat furnishes a set of visualization tools for illustrating the analysis results.Checking the spectra detected as outliers,we find that most of them are problematic data and only a few correspond to rare astronomical objects.We show two examples of these outliers,a spectrum with abnormal continuum and a spectrum with emission lines.Our work demonstrates that line index clustering is a good method for examining data quality and identifying rare objects.
