Photo-Met: a non-parametric method for estimating stellar metallicity from photometric observations

Getting spectra at good signal-to-noise ratios takes orders of magnitudes more time than photometric observations. Building on the technique developed for photometric redshift estimation of galaxies, we develop and demonstrate a non-parametric photometric method for estimating the chemical composition of galactic stars. We investigate the efficiency of our method using spectroscopically determined stellar metallicities from SDSS DR7. The technique is generic in the sense that it is not restricted to certain stellar types or stellar parameter ranges and makes it possible to obtain metallicities and error estimates for a much larger sample than spectroscopic surveys would allow. We find that our method performs well, especially for brighter stars and higher metallicities and, in contrast to many other techniques, we are able to reliably estimate the error of the predicted metallicities.Comment: 5 pages, 4 figures, accepted for publication in A

Cross-Identification of Stars with Unknown Proper Motions

The cross-identification of sources in separate catalogs is one of the most basic tasks in observational astronomy. It is, however, surprisingly difficult and generally ill-defined. Recently Budav\'ari & Szalay (2008) formulated the problem in the realm of probability theory, and laid down the statistical foundations of an extensible methodology. In this paper, we apply their Bayesian approach to stars that, we know, can move measurably on the sky, with detectable proper motion, and show how to associate their observations. We study models on a sample of stars in the Sloan Digital Sky Survey, which allow for an unknown proper motion per object, and demonstrate the improvements over the analytic static model. Our models and conclusions are directly applicable to upcoming surveys such as PanSTARRS, the Dark Energy Survey, Sky Mapper, and the LSST, whose data sets will contain hundreds of millions of stars observed multiple times over several years.Comment: 10 pages, 5 figure

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INST: L_08