Ted von Hippel

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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(More)
We investigate the application of neural networks to the automation of MK spectral classification. The data set for this project consists of a set of over 5000 optical (3800–5200Å) spectra obtained from objective prism plates from the Michigan Spectral Survey. These spectra, along with their two-dimensional MK classifications listed in the Michigan Henry(More)
We explore the application of artificial neural networks (ANNs) for the estimation of atmospheric parameters (Teff , log g , and [Fe/H]) for Galactic Fand G-type stars. The ANNs are fed with medium-resolution (∆λ ∼ 1 − 2 Å ) non flux-calibrated spectroscopic observations. From a sample of 279 stars with previous high-resolution determinations of(More)
Using HST and the WFPC2 we have acquired very deep Vand I-band photometry of stars in NGC 2420 and NGC 2477 to study cluster luminosity functions at approximately solar metallicity. We have determined these cluster luminosity functions down to MI = 10.5 (0.2 M⊙) and find that the luminosity function of NGC 2420 turns over at MI ≈ 9.0, and possibly stops(More)
Our mid-infrared survey of 124 white dwarfs with the Spitzer Space Telescope and the IRAC imager has revealed an infrared excess associatedwith thewhite dwarf WD2115 560 naturally explained by circumstellar dust. This object is the fourth white dwarf observed to have circumstellar dust. All four are DAZ white dwarfs, i.e., they have both photospheric Balmer(More)
We demonstrate a new Bayesian technique to invert color-magnitude diagrams of main sequence and white dwarf stars to reveal the underlying cluster properties of age, distance, metallicity, and line-of-sight absorption, as well as individual stellar masses. The advantages our technique has over traditional analyses of color-magnitude diagrams are(More)
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A reduced proper motion diagram utilizing Sloan Digital Sky Survey (SDSS) photometry and astrometry and USNO-B plate astrometry is used to separate cool white dwarf candidates from metal-weak, high-velocity main sequence Population II stars (subdwarfs) in the SDSS Data Release 2 imaging area. Follow-up spectroscopy using the Hobby-Eberly Telescope, the MMT,(More)
A new proper motion catalog is presented, combining the Sloan Digital Sky Survey (SDSS) with second epoch observations in the r band within a portion of the SDSS imaging footprint. The new observations were obtained with the 90prime camera on the Steward Observatory Bok 90 inch telescope, and the Array Camera on the U.S. Naval Observatory, Flagstaff(More)
We have initiated a project to classify stellar spectra automatically from high-dispersion objective prism plates. The automated technique presented here is a simple backpropagation neural network, and is based on the visual classification work of Houk. The plate material (Houk’s) is currently being digitized, and contains « 10 stars down to K æ 11 at æ 2-Â(More)