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Full-Text Articles in Physical Sciences and Mathematics
Automated Classification Of Stellar Spectra. Ii: Two-Dimensional Classification With Neural Networks And Principal Components Analysis, Ted Von Hippel, Coryn A.L. Bailer-Jones, Mike Irwin
Automated Classification Of Stellar Spectra. Ii: Two-Dimensional Classification With Neural Networks And Principal Components Analysis, Ted Von Hippel, Coryn A.L. Bailer-Jones, Mike Irwin
Publications
We investigate the application of neural networks to the automation of MK spec- tral classification. The data set for this project consists of a set of over 5000 optical (3800–5200°A) spectra obtained from objective prism plates from the Michigan Spec- tral Survey. These spectra, along with their two-dimensional MK classifications listed in the Michigan Henry Draper Catalogue, were used to develop supervised neural network classifiers. We show that neural networks can give accurate spectral type classifications (68 = 0.82 subtypes, rms= 1.09 subtypes) across the full range of spectral types present in the data set (B2–M7). We show also that …
Stellar Populations And The White Dwarf Mass Function: Connections To Supernova Ia Luminosities, Ted Von Hippel, G. D. Bothum, R. A. Schommer
Stellar Populations And The White Dwarf Mass Function: Connections To Supernova Ia Luminosities, Ted Von Hippel, G. D. Bothum, R. A. Schommer
Publications
We discuss the luminosity function of SNe Ia under the assumption that recent evidence for dispersion in this standard candle is related to variations in the white dwarf mass function (WDMF) in the host galaxies. We develop a simple parameterization of the WDMF as a function of age of a stellar population and apply this to galaxies of different morphological types. We show that this simplified model is consistent with the observed WDMF of Bergeron et al. (1992) for the solar neighborhood. Our simple models predict that WDMF variations can produce a range of more than 1.8 mag in MB(SN …
Physical Parameterization Of Stellar Spectra: The Neural Network Approach, Coryn A.L. Bailer-Jones, Ted Von Hippel, Mike Irwin, Gerard Gilmore
Physical Parameterization Of Stellar Spectra: The Neural Network Approach, Coryn A.L. Bailer-Jones, Ted Von Hippel, Mike Irwin, Gerard Gilmore
Publications
We present a technique which employs artificial neural networks to produce physical parameters for stellar spectra. A neural network is trained on a set of synthetic optical stellar spectra to give physical parameters (e.g. Teff, log g, [M/H]). The network is then used to produce physical parameters for real, observed spectra. Our neural networks are trained on a set of 155 synthetic spectra, generated using the spectrum program written by Gray (Gray & Corbally 1994, Gray & Arlt 1996). Once trained, the neural network is used to yield Teff for over 5000 B–K spectra extracted from a set of photographic …
Wiyn Data Distribution And Archiving, Rob Seaman, Ted Von Hippel
Wiyn Data Distribution And Archiving, Rob Seaman, Ted Von Hippel
Publications
The NOAO/IRAF Save the Bits archive has been operating for over three years at Kitt Peak National Observatory and at the National Solar Observatory's nighttime program. Since that time, the W. M. Keck Observatory and the Cerro Tololo Inter-American Observatory have also adopted the software. These first generation Save the Bits installations rely on Exabyte tapes as the archival medium, typically using pairs of drives to produce duplicate copies of the data for heightened protection against data loss. The upgrade of Save the Bits that is currently in progress to support writable CD-R drives is discussed. In addition to another …