We use the new Type Ia supernovae discovered by the Sloan Digital Sky Survey-II supernova survey, together with additional supernova data sets as well as observations of the cosmic microwave ...background and baryon acoustic oscillations to constrain cosmological models. This complements the standard cosmology analysis presented by Kessler et al. in that we discuss and rank a number of the most popular nonstandard cosmology scenarios. When this combined data set is analyzed using the MLCS2k2 light-curve fitter, we find that more exotic models for cosmic acceleration provide a better fit to the data than the Delta *LCDM model. For example, the flat Dvali-Gabadadze-Porrati model is ranked higher by our information-criteria (IC) tests than the standard model with a flat universe and a cosmological constant. When the supernova data set is instead analyzed using the SALT-II light-curve fitter, the standard cosmological-constant model fares best. This investigation of how sensitive cosmological model selection is to assumptions about, and within, the light-curve fitters thereby highlights the need for an improved understanding of these unresolved systematic effects. Our investigation also includes inhomogeneous Lemaitre-Tolman-Bondi (LTB) models. While our LTB models can be made to fit the supernova data as well as any other model, the extra parameters they require are not supported by our IC analysis. Finally, we explore more model-independent ways to investigate the cosmic expansion based on this new data set.
We present an analysis of the luminosity distances of Type Ia Supernovae (SNe) from the Sloan Digital Sky Survey-II (SDSS-II) SN Survey in conjunction with other intermediate-redshift (z < 0.4) ...cosmological measurements including redshift-space distortions from the Two-Degree Field Galaxy Redshift Survey (2dFGRS), the integrated Sachs–Wolfe (ISW) effect seen by the SDSS and the latest baryon acoustic oscillation (BAO) distance scale from both the SDSS and 2dFGRS. We have analysed the SDSS-II SN data alone using a variety of ‘model-independent’ methods and find evidence for an accelerating Universe at a >97 per cent level from this single data set. We find good agreement between the SN and BAO distance measurements, both consistent with a Λ-dominated cold dark matter cosmology, as demonstrated through an analysis of the distance duality relationship between the luminosity (dL) and angular diameter (dA) distance measures. We then use these data to estimate w within this restricted redshift range (z < 0.4). Our most stringent result comes from the combination of all our intermediate-redshift data (SDSS-II SNe, BAO, ISW and redshift-space distortions), giving w=−0.81+0.16−0.18 (stat) ± 0.15 (sys) and ΩM= 0.22+0.09−0.08 assuming a flat universe. This value of w and associated errors only change slightly if curvature is allowed to vary, consistent with constraints from the cosmic microwave background. We also consider more limited combinations of the geometrical (SN, BAO) and dynamical (ISW, redshift-space distortions) probes.
We present a new method for constructing three-dimensional mass maps from gravitational lensing shear data. We solve the lensing inversion problem using truncation of singular values (within the ...context of generalized least-squares estimation) without a priori assumptions about the statistical nature of the signal. This singular value framework allows a quantitative comparison between different filtering methods: we evaluate our method beside the previously explored Wiener-filter approaches. Our method yields near-optimal angular resolution of the lensing reconstruction and allows cluster sized halos to be de-blended robustly. It allows for mass reconstructions which are two to three orders of magnitude faster than the Wiener-filter approach; in particular, we estimate that an all-sky reconstruction with arcminute resolution could be performed on a timescale of hours. We find however that linear, non-parametric reconstructions have a fundamental limitation in the resolution achieved in the redshift direction.
Abstract
The Astropy Project supports and fosters the development of open-source and openly developed
Python
packages that provide commonly needed functionality to the astronomical community. A key ...element of the Astropy Project is the core package
astropy
, which serves as the foundation for more specialized projects and packages. In this article, we provide an overview of the organization of the Astropy project and summarize key features in the core package, as of the recent major release, version 2.0. We then describe the project infrastructure designed to facilitate and support development for a broader ecosystem of interoperable packages. We conclude with a future outlook of planned new features and directions for the broader Astropy Project.
We explore the utility of Karhunen-Loeve (KL) analysis in solving practical problems in the analysis of gravitational shear surveys. Shear catalogs from large-field weak-lensing surveys will be ...subject to many systematic limitations, notably incomplete coverage and pixel-level masking due to foreground sources. We develop a method to use two-dimensional KL eigenmodes of shear to interpolate noisy shear measurements across masked regions. We explore the results of this method with simulated shear catalogs, using statistics of high-convergence regions in the resulting map. We find that the KL procedure not only minimizes the bias due to masked regions in the field, it also reduces spurious peak counts from shape noise by a factor of ~3 in the cosmologically sensitive regime. This indicates that KL reconstructions of masked shear are not only useful for creating robust convergence maps from masked shear catalogs, but also offer promise of improved parameter constraints within studies of shear peak statistics.
As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements ...for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers.
Statistics, Data Mining, and Machine Learning in Astronomypresents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest.
Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data setsFeatures real-world data sets from contemporary astronomical surveysUses a freely available Python codebase throughoutIdeal for students and working astronomers
Background:
Although medication adherence is one of the most important aspects of the management of diabetes mellitus, low rates of adherence have been documented.
Objective:
This study sought to ...examine medication adherence among patients with diabetes mellitus in a managed care organization who were receiving antidiabetic monotherapy (metformin or glyburide), combination therapy (metformin and glyburide), or fixed-dose combination therapy (glyburide/metformin).
Methods:
Medication adherence was evaluated through a retrospective database analysis of pharmacy claims. The adherence rate was defined as the sum of the days' supply of oral antidiabetic medication obtained by the patient during the follow-up period divided by the total number of days in the designated follow-up period (180 days). Health plan members were included in the analysis if they had an index pharmacy claim for an oral antidiabetic medication between August 1 and December 31, 2000, were continuously enrolled in the health plan, and were aged ≥18 years. A 6-month pre-index period was used to classify patients as newly treated or previously treated. Patients were grouped according to their medication-use patterns.
Results:
After adjustment for potential confounding factors, including overall medication burden at index, there were no significant differences in adherence rates among 6502 newly treated patients receiving monotherapy, combination therapy, or fixed-dose combination therapy. Among the 1815 previously treated patients receiving glyburide or metformin monotherapy who required the addition of the alternative agent, resulting in combination therapy, adherence rates were significantly lower (54.0%; 95% CI, 0.52–0.55) than in the 105 patients receiving monotherapy who were switched to fixed-dose combination therapy (77.0%; 95% CI, 0.72–0.82). The 59 previously treated patients receiving combination therapy who were switched to fixed-dose combination therapy had a significant improvement in adherence after the switch (71.0% vs 87.0%;
P < 0.001).
Conclusions:
In a managed care organization, previously treated patients receiving monotherapy with an oral antidiabetic medication who required additional therapy exhibited significantly greater adherence when they were switched to fixed-dose combination therapy compared with combination therapy. Patients receiving combination therapy who were switched to fixed-dose combination therapy exhibited significantly greater adherence after the switch.
The Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of ...the Astropy Project is the core package astropy, which serves as the foundation for more specialized projects and packages. In this article, we provide an overview of the organization of the Astropy project and summarize key features in the core package, as of the recent major release, version 2.0. We then describe the project infrastructure designed to facilitate and support development for a broader ecosystem of interoperable packages. We conclude with a future outlook of planned new features and directions for the broader Astropy Project.
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