Spectrum Analysis of Bright Kepler late B- to early F- Stars

The Kepler satellite mission delivers single band-pass light curves of a huge number of stars observed in the Cygnus-Lyra region opening a new window for asteroseismology. In order to accomplish one of the preconditions for the asteroseismic modelling of the stars, we aim to derive fundamental parameters and individual abundances for a sample of 18 Gamma Dor (GD)/Delta Sct (DSct) and 8 SPB/beta Cep candidate stars in the Kepler satellite field of view. We use the spectral synthesis method to model newly obtained, high-resolution spectra of 26 stars in order to derive their fundamental parameters and individual abundances. The stars are then placed into the log(Teff)-log(g) diagram and the obtained spectroscopic classification is compared to the existing photometric one. For hot stars, the KIC temperatures appear to be systematically underestimated, in agreement with previous findings. We also find that the temperatures derived from our spectra agree reasonably well with those derived from the SED fitting. According to their position in the log(Teff)-log(g) diagram, two stars are expected GD stars, four stars are expected DSct stars, and four stars are possibly DSct stars at the blue edge of the instability strip. Two stars are confirmed SPB variables, and one star falls into the SPB instability region but its parameters might be biased by binarity. Two of the four stars that fall into the DSct instability region show GD-type oscillation in their light curves implying that GD-like oscillations are much more common among the DSct stars than is theoretically expected. Moreover, one of the stars located at the hot border of the DSct instability strip is classified as DSct-GD hybrid pulsator from its light curve analysis. Given that these findings are fully consistent with recent investigations, we conclude that a revision of the GD and DSct instability strips is essential.Comment: 14 pages, 14 figures, 6 tables; accepted for publication in MNRA

That fussy rag : two step

https://digitalcommons.library.umaine.edu/mmb-vp/5343/thumbnail.jp

WASP-1: A lithium- and metal-rich star with an oversized planet

In this paper we present our results of a comprehensive spectroscopicanalysis of WASP-1, the host star to the exoplanet WASP-1b. We derive T_eff = 6110 +/- 45 K, log g = 4.28 +/- 0.15, and [M/H] = 0.23 +/- 0.08, and also a high abundance of lithium, log n(Li) = 2.91 +/- 0.05. These parameters suggests an age for the system of 1-3 Gyr and a stellar mass of 1.25-1.35 M_sun. This means that WASP-1 has properties very similar to those of HD 149026, the host star for the highest density planet yet detected. Moreover, their planets orbit at comparable distances and receive comparable irradiating fluxes from their host stars. However, despite the similarity of WASP-1 with HD 149026, their planets have strongly different densities. This suggests that gas-giant planet density is not a simple function of host-star metallicity or of radiation environment at ages of ~2 Gyr.Comment: Accepted for publication in MNRAS. 6 pages, 4 figure

Forecasting Design Day Demand Using Extremal Quantile Regression

Extreme events occur rarely, making them difficult to predict. Extreme cold events strain natural gas systems to their limits. Natural gas distribution companies need to be prepared to satisfy demand on any given day that is at or warmer than an extreme cold threshold. The hypothetical day with temperature at this threshold is called the Design Day. To guarantee Design Day demand is satisfied, distribution companies need to determine the demand that is unlikely to be exceeded on the Design Day. We approach determining this demand as an extremal quantile regression problem. We review current methods for extremal quantile regression. We implement a quantile forecast to estimate the demand that has a minimal chance of being exceeded on the design day. We show extremal quantile regression to be more reliable than direct quantile estimation. We discuss the difficult task of evaluating a probabilistic forecast on rare events. Probabilistic forecasting is a quickly growing research topic in the field of energy forecasting. Our paper contributes to this field in three ways. First, we forecast quantiles during extreme cold events where data is sparse. Second, we forecast extremely high quantiles that have a very low probability of being exceeded. Finally, we provide a real world scenario on which to apply these techniques

An Economic Evaluation of Cattle Supplies and Slaughter Plant Capacity in New York and the Northeast Region

A.E. Res. 82-1