Combining historical and new hydrographic data

The NOAA National Ocean Service hydrographic surveys run between 1930 and 1965 have been digitized from the paper smooth sheets. The surveys since 1965 have been collected, processed, and stored in digital form. The new multibeam systems have been used since 1984 to cover over 100,000 square nautical miles of the Exclusive Economic Zone with overlapping swaths of digital soundings. Each of these multibeam surveys may contain millions of soundings. None of the above data has been assigned quality control tags by NOS, but they are stored by survey number, with indexes showing what younger data are available to supersede older data in any area.

Large digital databases, such as the Master Seaftoor Digital Database, are planned in connection with the Defense Hydrographic Initiative. It will be necessary to assign quality control ratings to the soundings in the databases. The detailed survey data may be indexed in the master database but maintained in distributed databases. The databases could supply historical sounding data in digital form for the planning, collection, processing, and evaluation of new survey data.

During the compilation of some bathymetric maps and nautical charts, it is necessary to junction and combine the newer multibeam surveys having total bottom coverage, with the more widely spaced historical data. Precedence is given to the newer hydrographic data, with some older data being removed as needed in order to provide a smooth transition between data sets. In applying multibeam data to nautical charts, it is necessary that actual soundings be positioned properly with respect to bottom contours, which may have been drawn using gridded values. The junctioning of historical and newer data sets is expected to be aided by the use of interactive cartographic workstations.     

An Artificial Neural Network approach to classify SDSS stellar spectra

We present a combined method to classify stellar spectra of the seventh data release (DR7) of the SDSS via an Artificial Neural Network (ANN), derive radial velocities and to estimate distances from an isochrone fitting technique. In total, we used 29 182 spectra of stars falling in the effective temperature range between 10000 and 5500 K, including white dwarfs. The targets were selected on the basis of SDSS colours. We compare our results not only with the SEGUE Stellar Parameter Pipeline output, but also with already published values and find excellent agreement. With new and extensive data sets from all‐sky ground based as well as satellite missions, our approach will become very important and efficient to analyse these information (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

New designs of survey telescopes

The immediate goal of modern observational astronomy is to monitor continuously the position and brightness of all objects in the sky brighter than ∼24th magnitude. This review describes wide‐field telescopes designed for this task – both existing and planned. Many systems are described for the first time (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Automated estimation of stellar fundamental parameters from low resolution spectra： the PLS method

PLS （Partial Least Squares regression） is introduced into an automatic estimation of fundamental stellar spectral parameters. It extracts the most correlative spectral component to the parameters （Teff, log g and [Fe/H]）, and sets up a linear regression function from spectra to the corresponding parameters. Considering the properties of stellar spectra and the PLS algorithm, we present a piecewise PLS regression method for estimation of stellar parameters, which is composed of one PLS model for Teff, and seven PLS models for log g and [Fe/H] estimation. Its performance is investigated by large experiments on flux calibrated spectra and continuum normalized spectra at different signal-to-noise ratios （SNRs） and resolutions. The results show that the piecewise PLS method is robust for spectra at the medium resolution of 0.23 nm. For low resolution 0.5 nm and 1 nm spectra, it achieves competitive results at higher SNR. Experiments using ELODIE spectra of 0.23 nm resolution illustrate that our piecewise PLS models trained with MILES spectra are efficient for O ～ G stars： for flux calibrated spectra, the systematic offsets are 3.8%, 0.14 dex, and -0.09 dex for Teff, log g and [Fe/H], with error scatters of 5.2%, 0.44 dex and 0.38 dex, respectively; for continuum normalized spectra, the systematic offsets are 3.8%, 0.12dex, and -0.13 dex for Teff, log g and [Fe/H], with error scatters of 5.2%, 0.49 dex and 0.41 dex, respectively. The PLS method is rapid, easy to use and does not rely as strongly on the tightness of a parameter grid of templates to reach high precision as Artificial Neural Networks or minimum distance methods do.