Faraday rotation (rotation measure [RM]) probes of magnetic fields in the universe are sensitive to cosmological and evolutionary effects as z increases beyond $1 because of the scalings of electron density and magnetic fields, and the growth in the number of expected intersections with galaxy-scale intervenors, dN /dz. In this new global analysis of an unprecedented large sample of RMs of high-latitude quasars extending out to z $ 3:7, we find that the distribution of RM broadens with redshift in the 20Y80 rad m À2 range, despite the (1 þ z) À2 wavelength dilution expected in the observed Faraday rotation. Our results indicate that the universe becomes increasingly ''Faraday-opaque'' to sources beyond z $ 2; that is, as z increases, progressively fewer sources are found with a ''small'' RM in the observer's frame. This is in contrast to sources at z P1. They suggest that the environments of galaxies were significantly magnetized at high redshifts, with magnetic field strengths that were at least as strong within a few Gyr of the big bang as at the current epoch. We separately investigate a simple unevolving toy model in which the RM is produced by Mg ii absorber systems, and find that it can approximately reproduce the observed trend with redshift. An additional possibility is that the intrinsic RM associated with the radio sources was much higher in the past, and we show that this is not a trivial consequence of the higher radio luminosities of the high-redshift sources.
This is an author-produced, peer-reviewed version of this article. AbstractEnvironmental sensors have been deployed in various cities for early detection of contaminant releases into the atmosphere. Event reconstruction and improved dispersion modeling capabilities are needed to estimate the extent of contamination, which is required to implement effective strategies in emergency management. To this end, a stochastic event reconstruction capability that can process information from an environmental sensor network is developed. A probability model is proposed to take into account both zero and non-zero concentration measurements that can be available from a sensor network because of a sensor's specified limit of detection. The inference is based on the Bayesian paradigm with Markov chain Monte Carlo (MCMC) sampling. Fast-running Gaussian plume dispersion models are adopted as the forward model in the Bayesian inference approach to achieve rapid-response event reconstructions. The Gaussian plume model is substantially enhanced by introducing stochastic parameters in its turbulent diffusion parameterizations and estimating them within the Bayesian inference framework. Additionally, parameters of the likelihood function are estimated in a principled way using data and prior probabilities to avoid tuning in the overall method, The event reconstruction method is successfully validated for both real and synthetic dispersion problems, and posterior distributions of the model parameters are used to generate probabilistic plume envelopes with specified confidence levels to aid emergency decisions.Key words: Bayesian Statistics, Event Reconstruction, Source Characterization, Gaussian Plume Models, Markov chain Monte Carlo (MCMC) Preprint submitted to Atmospheric Environment 27 April 2008This is an author-produced, peer-reviewed version of this article. The final, definitive version of this document can be found online at Atmospheric Environment,
The response of glaciers to climate change has major implications for sea-level change and water resources around the globe. Large-scale glacier evolution models are used to project glacier runoff and mass loss, but are constrained by limited observations, which result in models being over-parameterized. Recent systematic geodetic mass-balance observations provide an opportunity to improve the calibration of glacier evolution models. In this study, we develop a calibration scheme for a glacier evolution model using a Bayesian inverse model and geodetic mass-balance observations, which enable us to quantify model parameter uncertainty. The Bayesian model is applied to each glacier in High Mountain Asia using Markov chain Monte Carlo methods. After 10,000 steps, the chains generate a sufficient number of independent samples to estimate the properties of the model parameters from the joint posterior distribution. Their spatial distribution shows a clear orographic effect indicating the resolution of climate data is too coarse to resolve temperature and precipitation at high altitudes. Given the glacier evolution model is over-parameterized, particular attention is given to identifiability and the need for future work to integrate additional observations in order to better constrain the plausible sets of model parameters.
Top predators, such as salmon sharks (Lamna ditropis), can influence the abundance and population structure of organisms at lower trophic levels through direct effects, such as predation mortality, and indirect interactions. As a first step towards better understanding the average annual prey consumption for individual adult salmon sharks, we bracketed consumption estimates using three methods: (1) daily ration requirement; (2) bioenergetic mass balance; and (3) a Bayesian model of shark growth. In the first method, we applied ration estimates for related lamnid shark species that yielded salmon shark estimates of 1461 and 2202kgyear–1. The second method used a mass–balance technique to incorporate life history information from salmon sharks and physiological parameters from other species and produced estimates of 1870, 2070, 1610 and 1762kgyear–1, depending on assumed diet. Growth modelling used salmon shark growth histories and yielded estimates of 16900 or 20800kgyear–1, depending on assumed assimilation efficiency. Of the consumption estimates, those from the mass–balance technique may be the most realistic because they incorporated salmon shark life history data and do not produce extreme values. Taken as a whole, these estimates suggest that salmon sharks have similar energetic requirements to piscivorous marine mammals.
Our primary goal is to obtain a smoothed summary estimate of the magnetic field generated in and near to the Milky Way by using Faraday rotation measures (RM's). Each RM in our data set provides an integrated measure of the effect of the magnetic field along the entire line of sight to an extragalactic radio source. The ability to estimate the magnetic field generated locally by our galaxy and its environs will help astronomers distinguish local versus distant properties of the universe. RM's can be considered analogous to geostatistical data on a sphere. In order to model such data, we employ a Bayesian process convolution approach which uses Markov chain Monte Carlo (MCMC) for estimation and prediction. Complications arise due to contamination in the RM measurements, and we resolve these by means of a mixture prior on the errors.
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