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Full-Text Articles in Stars, Interstellar Medium and the Galaxy
Three-Dimensional Spectral Classification Of Low-Metallicity Stars Using Artificial Neural Networks, Shawn Snider, Ted Von Hippel, Et Al.
Three-Dimensional Spectral Classification Of Low-Metallicity Stars Using Artificial Neural Networks, Shawn Snider, Ted Von Hippel, Et Al.
Ted von Hippel
We explore the application of artificial neural networks (ANNs) for the estimation of atmospheric parameters (Teff, log g, and [Fe/H]) for Galactic F- and G-type stars. The ANNs are fed with medium-resolution (Δλ ~ 1-2 Å) non-flux-calibrated spectroscopic observations. From a sample of 279 stars with previous high-resolution determinations of metallicity and a set of (external) estimates of temperature and surface gravity, our ANNs are able to predict Teff with an accuracy of σ(Teff) = 135-150 K over the range 4250 ≤ Teff ≤ 6500 K, log g with an accuracy …
Precise Ages Of Field Stars From White Dwarf Companions, M. Fouesneau, H-W. Rix, T. Von Hippel, D. W. Hogg, H. Tian
Precise Ages Of Field Stars From White Dwarf Companions, M. Fouesneau, H-W. Rix, T. Von Hippel, D. W. Hogg, H. Tian
Ted von Hippel
Observational tests of stellar and Galactic chemical evolution call for the joint knowledge of a star’s physical parameters, detailed element abundances, and precise age. For cool main-sequence (MS) stars the abundances of many elements can be measured from spectroscopy, but ages are very hard to determine. The situation is different if the MS star has a white dwarf (WD) companion and a known distance, as the age of such a binary system can then be determined precisely from the photometric properties of the cooling WD. As a pilot study for obtaining precise age determinations of field MS stars, we identify …
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
Ted von Hippel
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 …
Semi-Automated Extraction Of Digital Objective Prism Spectra, Coryn A.L. Bailer-Jones, Ted Von Hippel, Mike Irwin
Semi-Automated Extraction Of Digital Objective Prism Spectra, Coryn A.L. Bailer-Jones, Ted Von Hippel, Mike Irwin
Ted von Hippel
We describe a method for the extraction of spectra from high dispersion objective prism plates. Our method is a catalogue driven plate solution approach, making use of the Right Ascension and Declination coordinates for the target objects. In contrast to existing methods of photographic plate reduction, we digitize the entire plate and extract spectra off-line. This approach has the advantages that it can be applied to CCD objective prism images, and spectra can be re-extracted (or additional spectra extracted) without having to re-scan the plate. After a brief initial interactive period, the subsequent reduction procedure is completely automatic, resulting in …