Classification of LAMOST DR3 FGK Spectra

Ranjan Gupta1*, H.P. Singh2 and Yue Wu3
11IUCAA, Post Bag 4, Ganeshkhind, Pune-411007,India
2University of Delhi, New Delhi-110007, India
3NAOC,Chinese Academy of Sciences, 20A Datun Road, Beijing-100012, China

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The Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) is producing a very large database which consists of spectra from the Chinese 4-meter telescope. LAMOST Data Release 3 (DR3 contains over 5X107 spectra, of which 5.4X106 are stellar spectra. In this preliminary work we have used only the F, G and K spectral types of stars with S/N > 30. Our data set consists of 286,283 spectra that have been classified using Automated Supervised Neural Network (ANNs); this forms the test set, while the training set consists of spectra from the MILES spectral library. Of the three ANN tools used (Tree, Forest and Neural), the best performance was seen for the Tree based classifier; it returned a classification accuracy of 71.4% correct spectral types and an error of 4.61% spectral sub-types. The luminosity classes are not known for the vast majority of LAMOST spectra, but our automated schemes provide this information along with a confidence estimate (with corresponding classification probabilities) for each spectrum. Future work will involve (i) using more classification tools, (ii) improving the classification accuracies, and (iii) applying these upgrades to future LAMOST data releases.

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Keywords : stellar spectra; classification; automated schemes; neural networks