Skycorr: A general tool for spectroscopic sky subtraction

Airglow emission lines, which dominate the optical-to-near-IR sky radiation, show strong, line-dependent variability on various time scales. Therefore, the subtraction of the sky background in the affected wavelength regime becomes a problem if plain sky spectra have to be taken at a different time as the astronomical data. A solution of this issue is the physically motivated scaling of the airglow lines in the plain sky data to fit the sky lines in the object spectrum. We have developed a corresponding instrument-independent approach based on one-dimensional spectra. Our code skycorr separates sky lines and sky/object continuum by an iterative approach involving a line finder and airglow line data. The sky lines are grouped according to their expected variability. The line groups in the sky data are then scaled to fit the sky in the science data. Required pixel-specific weights for overlapping groups are taken from a comprehensive airglow model. Deviations in the wavelength calibration are corrected by fitting Chebyshev polynomials and rebinning via asymmetric damped sinc kernels. The scaled sky lines and the sky continuum are subtracted separately. VLT X-Shooter data covering time intervals from two minutes to about one year were selected to illustrate the performance. Except for short time intervals of a few minutes, the sky line residuals were several times weaker than for sky subtraction without fitting. Further tests show that skycorr performs consistently better than the method of Davies (2007) developed for VLT SINFONI data.Comment: 17 pages, 18 figures, accepted for publication in A&

Investigating the Filled Gel Model in Cheddar Cheese Through Use of Sephadex Beads

Cheese can be modeled as a filled gel whereby milkfat globules are dispersed in a casein gel network. We determined the filler effects using Sephadex beads (GE Healthcare Life Sciences, Pittsburgh, PA) as a model filler particle. Ideally, such a model could be used to test novel filler particles to replace milkfat in low-fat cheese. Low-filler (6% particles), reduced-filler (16%), and full-filler (33%) cheeses were produced using either Sephadex beads of varying sizes (20 to 150 μm diameter) or milkfat. Small- and large-strain rheological tests were run on each treatment at 8, 12, and 18 wk after cheese manufacturing. Differences in rheological properties were caused primarily by the main effects of filler volume and type (milkfat vs. Sephadex), whereas filler size had no obvious effect. All treatments showed a decrease in deformability and an increase in firmness as filler volume increased above 25%, although the beads exhibited a greater reinforcing effect and greater energy recovery than milkfat

Molecfit: A general tool for telluric absorption correction. I. Method and application to ESO instruments

Context: The interaction of the light from astronomical objects with the constituents of the Earth's atmosphere leads to the formation of telluric absorption lines in ground-based collected spectra. Correcting for these lines, mostly affecting the red and infrared region of the spectrum, usually relies on observations of specific stars obtained close in time and airmass to the science targets, therefore using precious observing time. Aims: We present molecfit, a tool for correcting for telluric absorption lines based on synthetic modelling of the Earth's atmospheric transmission. Molecfit is versatile and can be used with data obtained with various ground-based telescopes and instruments. Methods: Molecfit combines a publicly available radiative transfer code, a molecular line database, atmospheric profiles, and various kernels to model the instrument line spread function. The atmospheric profiles are created by merging a standard atmospheric profile representative of a given observatory's climate, of local meteorological data, and of dynamically retrieved altitude profiles for temperature, pressure, and humidity. We discuss the various ingredients of the method, its applicability, and its limitations. We also show examples of telluric line correction on spectra obtained with a suite of ESO Very Large Telescope (VLT) instruments. Results: Compared to previous similar tools, molecfit takes the best results for temperature, pressure, and humidity in the atmosphere above the observatory into account. As a result, the standard deviation of the residuals after correction of unsaturated telluric lines is frequently better than 2% of the continuum. Conclusion: Molecfit is able to accurately model and correct for telluric lines over a broad range of wavelengths and spectral resolutions. (Abridged)Comment: 18 pages, 13 figures, 5 tables, accepted for publication in Astronomy and Astrophysic

Spin-structures of N-boson systems with nonzero spins - an analytically solvable model with pairing force

A model is proposed to study the possible pairing structures of N-boson systems with nonzero spin. Analytical solutions have been obtained. The emphasis is placed on the spin-structures of ground states with attractive or repulsive pairing force, and with or without the action of a magnetic field. A quantity (an analogue of the two-body density function) is defined to study the spin-correlation between two bosons in N-body systems. The excitation of the system has also been studied.Comment: 10 pages, 2 figures, accepted by Few-Body System

Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

Citation: Peralta, N.R.; Assefa, Y.; Du, J.; Barden, C.J.; Ciampitti, I.A. Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield. Remote Sens. 2016, 8, 848.This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth (http://s2.boku.eodc.eu/). Using the European Space Agency’s (ESA) Sen2Cor algorithm, the platform processes ESA’s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data (http://www.eodc.eu). Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates value-added products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value)

Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at midgrowing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington) of Kansas (total of 457 ha). Three basic tests were conducted on the data: (1) spatial dependence on each of the yield and vegetation indices (VIs) using Moran’s I test; (2) model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS) and spatial econometric (SPL) models; and (3) model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran’s I test) for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG) was tested positive and statistically significant for most of the fields (p < 0.05), except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02) was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to inform precision agricultural management decisions.Sociedad Argentina de Informática e Investigación Operativ

Stellar science from a blue wavelength range - A possible design for the blue arm of 4MOST

From stellar spectra, a variety of physical properties of stars can be derived. In particular, the chemical composition of stellar atmospheres can be inferred from absorption line analyses. These provide key information on large scales, such as the formation of our Galaxy, down to the small-scale nucleosynthesis processes that take place in stars and supernovae. By extending the observed wavelength range toward bluer wavelengths, we optimize such studies to also include critical absorption lines in metal-poor stars, and allow for studies of heavy elements (Z>38) whose formation processes remain poorly constrained. In this context, spectrographs optimized for observing blue wavelength ranges are essential, since many absorption lines at redder wavelengths are too weak to be detected in metal-poor stars. This means that some elements cannot be studied in the visual-redder regions, and important scientific tracers and science cases are lost. The present era of large public surveys will target millions of stars. Here we describe the requirements driving the design of the forthcoming survey instrument 4MOST, a multi-object spectrograph commissioned for the ESO VISTA 4m-telescope. We focus here on high-density, wide-area survey of stars and the science that can be achieved with high-resolution stellar spectroscopy. Scientific and technical requirements that governed the design are described along with a thorough line blending analysis. For the high-resolution spectrograph, we find that a sampling of >2.5 (pixels per resolving element), spectral resolution of 18000 or higher, and a wavelength range covering 393-436 nm, is the most well-balanced solution for the instrument. A spectrograph with these characteristics will enable accurate abundance analysis (+/-0.1 dex) in the blue and allow us to confront the outlined scientific questions. (abridged)Comment: 14 pages, 8 figures, accepted for publication in A

Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at midgrowing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington) of Kansas (total of 457 ha). Three basic tests were conducted on the data: (1) spatial dependence on each of the yield and vegetation indices (VIs) using Moran’s I test; (2) model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS) and spatial econometric (SPL) models; and (3) model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran’s I test) for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG) was tested positive and statistically significant for most of the fields (p < 0.05), except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02) was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to inform precision agricultural management decisions.Sociedad Argentina de Informática e Investigación Operativ

Evolution of optically faint AGN from COMBO-17 and GEMS

We have mapped the AGN luminosity function and its evolution between z=1 and z=5 down to apparent magnitudes of $R<24$. Within the GEMS project we have analysed HST-ACS images of many AGN in the Extended Chandra Deep Field South, enabling us to assess the evolution of AGN host galaxy properties with cosmic time.Comment: to appear in proceedings 'Multiwavelength AGN Surveys', Cozumel 200