University of Arizona

About: University of Arizona is a education organization based out in Tucson, Arizona, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 63805 authors who have published 155998 publications receiving 6854915 citations. The organization is also known as: UA & U of A.

The Second Data Release of the Sloan Digital Sky Survey

Abstract: The Sloan Digital Sky Survey (SDSS) has validated and made publicly available its Second Data Release. This data release consists of 3324 deg2 of five-band (ugriz) imaging data with photometry for over 88 million unique objects, 367,360 spectra of galaxies, quasars, stars, and calibrating blank sky patches selected over 2627 deg2 of this area, and tables of measured parameters from these data. The imaging data reach a depth of r ≈ 22.2 (95% completeness limit for point sources) and are photometrically and astrometrically calibrated to 2% rms and 100 mas rms per coordinate, respectively. The imaging data have all been processed through a new version of the SDSS imaging pipeline, in which the most important improvement since the last data release is fixing an error in the model fits to each object. The result is that model magnitudes are now a good proxy for point-spread function magnitudes for point sources, and Petrosian magnitudes for extended sources. The spectroscopy extends from 3800 to 9200 A at a resolution of 2000. The spectroscopic software now repairs a systematic error in the radial velocities of certain types of stars and has substantially improved spectrophotometry. All data included in the SDSS Early Data Release and First Data Release are reprocessed with the improved pipelines and included in the Second Data Release. Further characteristics of the data are described, as are the data products themselves and the tools for accessing them.

A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters

Abstract: Author(s): Vrugt, JA; Gupta, HV; Bouten, W; Sorooshian, S | Abstract: Markov Chain Monte Carlo (MCMC) methods have become increasingly popular for estimating the posterior probability distribution of parameters in hydrologic models. However, MCMC methods require the a priori definition of a proposal or sampling distribution, which determines the explorative capabilities and efficiency of the sampler and therefore the statistical properties of the Markov Chain and its rate of convergence. In this paper we present an MCMC sampler entitled the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA), which is well suited to infer the posterior distribution of hydrologic model parameters. The SCEM-UA algorithm is a modified version of the original SCE-UA global optimization algorithm developed by Duan et al. [1992]. The SCEM-UA algorithm operates by merging the strengths of the Metropolis algorithm, controlled random search, competitive evolution, and complex shuffling in order to continuously update the proposal distribution and evolve the sampler to the posterior target distribution. Three case studies demonstrate that the adaptive capability of the SCEM-UA algorithm significantly reduces the number of model simulations needed to infer the posterior distribution of the parameters when compared with the traditional Metropolis-Hastings samplers.

A Quantitative Comparison of the Small Magellanic Cloud, Large Magellanic Cloud, and Milky Way Ultraviolet to Near-Infrared Extinction Curves

Abstract: We present an exhaustive quantitative comparison of all the known extinction curves in the Small and Large Magellanic Clouds (SMC and LMC) with our understanding of the general behavior of Milky Way extinction curves. The RV-dependent CCM relationship of Cardelli, Clayton, and Mathis and the sample of extinction curves used to derive this relationship are used to describe the general behavior of Milky Way extinction curves. The ultraviolet portion of the SMC and LMC extinction curves are derived from archival IUE data, except for one new SMC extinction curve, which was measured using Hubble Space Telescope Space Telescope Imaging Spectrograph observations. The optical extinction curves are derived from new (for the SMC) and literature UBVRI photometry (for the LMC). The near-infrared extinction curves are calculated mainly from 2MASS photometry supplemented with DENIS and new JHK photometry. For each extinction curve, we give RV = A(V)/E(B - V) and N(H I) values that probe the same dust column as the extinction curve. We compare the properties of the SMC and LMC extinction curves with the CCM relationship three different ways: each curve by itself, the behavior of extinction at different wavelengths with RV, and the behavior of the extinction curve Fitzpatrick and Massa fit parameters with RV. As has been found previously, we find that a small number of LMC extinction curves are consistent with the CCM relationship, but the majority of the LMC and all the SMC curves do not follow the CCM relationship. For the first time, we find that the CCM relationship seems to form a bound on the properties of all the LMC and SMC extinction curves. This result strengthens the picture dust extinction curves exhibit of a continuum of properties between those found in the Milky Way and the SMC bar. Tentative evidence based on the behavior of the extinction curves with dust-to-gas ratio suggests that the continuum of dust extinction curves is possibly caused by the environmental stresses of nearby star formation activity.