Incrementalism v. Disjuncture: The President and American Political Development

Reviewing J. David Alvis, Jeremy D. Bailey, and F. Flagg Taylor, The Contested Removal Power, 1789-2010; and Michael J. Gerhardt, The Forgotten Presidents: Their Untold Constitutional Legacy

Uncertainty in 2-point correlation function estimators and BAO detection in SDSS DR7

We study the uncertainty in different two-point correlation function (2PCF) estimators in currently available galaxy surveys. This is motivated by the active subject of using the baryon acoustic oscillations (BAOs) feature in the correlation function as a tool to constrain cosmological parameters, which requires a fine analysis of the statistical significance. We discuss how estimators are affected by both the uncertainty in the mean density $\bar{n}$ and the integral constraint $\frac{1}{V^2}\int_{V^2} \hat{\xi} (r) d^3r =0$ which necessarily causes a bias. We quantify both effects for currently available galaxy samples using simulated mock catalogues of the Sloan Digital Sky Survey (SDSS) following a lognormal model, with a Lambda-Cold Dark Matter ($\Lambda\text{CDM}$) correlation function and similar properties as the samples (number density, mean redshift for the $\Lambda\text{CDM}$ correlation function, survey geometry, mass-luminosity bias). Because we need extensive simulations to quantify small statistical effects, we cannot use realistic N-body simulations and some physical effects are neglected. Our simulations still enable a comparison of the different estimators by looking at their biases and variances. We also test the reliability of the BAO detection in the SDSS samples and study the compatibility of the data results with our $\Lambda\text{CDM}$ simulations.Comment: 14 pages, 6 figures, 3 table

Sub-kilometre scale distribution of snow depth on Arctic sea ice from Soviet drifting stations

The sub-kilometre scale distribution of snow depth on Arctic sea ice impacts atmosphere-ice fluxes of energy and mass, and is of importance for satellite estimates of sea-ice thickness from both radar and lidar altimeters. While information about the mean of this distribution is increasingly available from modelling and remote sensing, the full distribution cannot yet be resolved. We analyse 33 539 snow depth measurements from 499 transects taken at Soviet drifting stations between 1955 and 1991 and derive a simple statistical distribution for snow depth over multi-year ice as a function of only the mean snow depth. We then evaluate this snow depth distribution against snow depth transects that span first-year ice to multiyear ice from the MOSAiC, SHEBA and AMSR-Ice field campaigns. Because the distribution can be generated using only the mean snow depth, it can be used in the downscaling of several existing snow depth products for use in flux modelling and altimetry studies

From Cancer to Diarrhea: The Moving Target of Public Concern about Environmental Health Risks

Public concern about the environment can be unpredictable because it is influenced by numerous factors. Environmental health issues often emerge as important because the public is worried about their health especially when it comes to cancer. Public fear of cancer from environmental exposures is reinforced by many of the US regulations that set pollutant limits based on reducing the risk of cancers rather than other health outcomes. While fear of cancer will never dissipate, recent foodborne outbreaks are contributing to raising public awareness of the health effects from microbes. This paper adds to the dialogue about the challenges of enhancing public understanding of environmental health issues. Internal factors, such as worry, that contribute to public outrage are sometimes more important than external factors such as the media. In addition, relying on the media to inform the public about imminent public health risks may be an ineffective approach to enhancing understanding. In the end, scientists and risk communicators are forced to compete with politicians who are often very effective at manipulating public understanding of risk

The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: measuring structure growth using passive galaxies

We explore the benefits of using a passively evolving population of galaxies to measure the evolution of the rate of structure growth between z=0.25 and z=0.65 by combining data from the SDSS-I/II and SDSS-III surveys. The large-scale linear bias of a population of dynamically passive galaxies, which we select from both surveys, is easily modeled. Knowing the bias evolution breaks degeneracies inherent to other methodologies, and decreases the uncertainty in measurements of the rate of structure growth and the normalization of the galaxy power-spectrum by up to a factor of two. If we translate our measurements into a constraint on sigma_8(z=0) assuming a concordance cosmological model and General Relativity (GR), we find that using a bias model improves our uncertainty by a factor of nearly 1.5. Our results are consistent with a flat Lambda Cold Dark Matter model and with GR.Comment: Accepted for publication in MNRAS (clarifications added, results and conclusions unchanged

The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: Analysis of potential systematics

We analyze the density field of galaxies observed by the Sloan Digital Sky Survey (SDSS)-III Baryon Oscillation Spectroscopic Survey (BOSS) included in the SDSS Data Release Nine (DR9). DR9 includes spectroscopic redshifts for over 400,000 galaxies spread over a footprint of 3,275 deg^2. We identify, characterize, and mitigate the impact of sources of systematic uncertainty on large-scale clustering measurements, both for angular moments of the redshift-space correlation function and the spherically averaged power spectrum, P(k), in order to ensure that robust cosmological constraints will be obtained from these data. A correlation between the projected density of stars and the higher redshift (0.43 < z < 0.7) galaxy sample (the `CMASS' sample) due to imaging systematics imparts a systematic error that is larger than the statistical error of the clustering measurements at scales s > 120h^-1Mpc or k < 0.01hMpc^-1. We find that these errors can be ameliorated by weighting galaxies based on their surface brightness and the local stellar density. We use mock galaxy catalogs that simulate the CMASS selection function to determine that randomly selecting galaxy redshifts in order to simulate the radial selection function of a random sample imparts the least systematic error on correlation function measurements and that this systematic error is negligible for the spherically averaged correlation function. The methods we recommend for the calculation of clustering measurements using the CMASS sample are adopted in companion papers that locate the position of the baryon acoustic oscillation feature (Anderson et al. 2012), constrain cosmological models using the full shape of the correlation function (Sanchez et al. 2012), and measure the rate of structure growth (Reid et al. 2012). (abridged)Comment: Matches version accepted by MNRAS. Clarifications and references have been added. See companion papers that share the "The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey:" titl

The clustering of galaxies at z~0.5 in the SDSS-III Data Release 9 BOSS-CMASS sample: a test for the LCDM cosmology

We present results on the clustering of 282,068 galaxies in the Baryon Oscillation Spectroscopic Survey (BOSS) sample of massive galaxies with redshifts 0.4<z<0.7 which is part of the Sloan Digital Sky Survey III project. Our results cover a large range of scales from ~0.5 to ~90 Mpc/h. We compare these estimates with the expectations of the flat LCDM cosmological model with parameters compatible with WMAP7 data. We use the MultiDark cosmological simulation together with a simple halo abundance matching technique, to estimate galaxy correlation functions, power spectra, abundance of subhaloes and galaxy biases. We find that the LCDM model gives a reasonable description to the observed correlation functions at z~0.5, which is a remarkably good agreement considering that the model, once matched to the observed abundance of BOSS galaxies, does not have any free parameters. However, we find a deviation (>~10%) in the correlation functions for scales less than ~1 Mpc/h and ~10-40 Mpc/h. A more realistic abundance matching model and better statistics from upcoming observations are needed to clarify the situation. We also estimate that about 12% of the "galaxies" in the abundance-matched sample are satellites inhabiting central haloes with mass M>~1e14 M_sun/h. Using the MultiDark simulation we also study the real space halo bias b(r) of the matched catalogue finding that b=2.00+/-0.07 at large scales, consistent with the one obtained using the measured BOSS projected correlation function. Furthermore, the linear large-scale bias depends on the number density n of the abundance-matched sample as b=-0.048-(0.594+/-0.02)*log(n/(h/Mpc)^3). Extrapolating these results to BAO scales we measure a scale-dependent damping of the acoustic signal produced by non-linear evolution that leads to ~2-4% dips at ~3 sigma level for wavenumbers k>~0.1 h/Mpc in the linear large-scale bias.Comment: Replaced to match published version. Typos corrected; 25 pages, 17 figures, 9 tables. To appear in MNRAS. Correlation functions (projected and redshift-space) and correlation matrices of CMASS presented in Appendix B. Correlation and covariance data for the combined CMASS sample can be downloaded from http://www.sdss3.org/science/boss_publications.ph