Previous | Session 29 | Next | Author Index | Block Schedule
S.M. Kattner (Univ. of Wyoming), J. Glaspey (MMT Observatory)
With the multitude of stellar objects in the sky, we have investigated development of an automated spectral classification system within IRAF to assist in the analysis of small to moderate sized spectroscopic datasets. Using data mining, we extracted 108 standard, sharp-lined B, A, and F stars from the NOAO Digital Library, and measured equivalent widths for 65 prominent lines in the 3000-7000 Angstrom range. Spectral type versus equivalent width intensity was plotted in order to retrieve the lines that demonstrated a clear relationship. For each of the 29 spectral features exhibiting a good correlation between spectral type and line strength, we could fit the data with a polynomial of order three to five. These polynomial fits were then used to predict the spectral types for a separate sample of objects from the NOAO Digital Library. From the comparison of the second data set with the first, we found that several lines could be used for an automated classification system, allowing us good reason to believe that such a system can eventually be established. Kattner’s research was supported by the NOAO/KPNO Research Experiences for Undergraduates (REU) Program, which is funded by the National Science Foundation through Scientific Program Order No. 3 (AST-0243875) of the Cooperative Agreement No. AST-0132798 between the Association of Universities for Research in Astronomy (AURA) and the NSF.
Previous | Session 29 | Next
Bulletin of the American Astronomical Society, 37 #4
© 2005. The American Astronomical Soceity.