Lensing and Supernovae: Quantifying The Bias on the Dark Energy Equation of State

The gravitational magnification and demagnification of Type Ia supernovae (SNe) modify their positions on the Hubble diagram, shifting the distance estimates from the underlying luminosity-distance relation. This can introduce a systematic uncertainty in the dark energy equation of state (EOS) estimated from SNe, although this systematic is expected to average away for sufficiently large data sets. Using mock SN samples over the redshift range $0 < z \leq 1.7$ we quantify the lensing bias. We find that the bias on the dark energy EOS is less than half a percent for large datasets ($\gtrsim$ 2,000 SNe). However, if highly magnified events (SNe deviating by more than 2.5$\sigma$) are systematically removed from the analysis, the bias increases to $\sim$ 0.8%. Given that the EOS parameters measured from such a sample have a 1$\sigma$ uncertainty of 10%, the systematic bias related to lensing in SN data out to $z \sim 1.7$ can be safely ignored in future cosmological measurements.Comment: 5 pages, 4 figures; one figure and references added; minor modifications to text; reflects version accepted for publication in Ap

Beyond Two Dark Energy Parameters

Our ignorance of the dark energy is generally described by a two-parameter equation of state. In these approaches a particular {\it ad hoc} functional form is assumed, and only two independent parameters are incorporated. We propose a model-independent, multi-parameter approach to fitting the dark energy, and show that next-generation surveys will constrain the equation of state in three or more independent redshift bins to better than 10%. Future knowledge of the dark energy will surpass two numbers (e.g., [$w_0$,$w_1$] or [$w_0$,$w_a$]), and we propose a more flexible approach to the analysis of present and future data.Comment: 4 pages, 1 figure; Discussion expanded to include next-generation BAO surveys and possible systematics in SN surveys; reflects version accepted for publication in Phys. Rev. Let

Nonequilibrium phase transition in the coevolution of networks and opinions

Models of the convergence of opinion in social systems have been the subject of a considerable amount of recent attention in the physics literature. These models divide into two classes, those in which individuals form their beliefs based on the opinions of their neighbors in a social network of personal acquaintances, and those in which, conversely, network connections form between individuals of similar beliefs. While both of these processes can give rise to realistic levels of agreement between acquaintances, practical experience suggests that opinion formation in the real world is not a result of one process or the other, but a combination of the two. Here we present a simple model of this combination, with a single parameter controlling the balance of the two processes. We find that the model undergoes a continuous phase transition as this parameter is varied, from a regime in which opinions are arbitrarily diverse to one in which most individuals hold the same opinion. We characterize the static and dynamical properties of this transition

Structure of microbial communities in Sphagnum peatlands and effect of atmospheric carbon dioxide enrichment

Little is known about the structure of microbial communities in Sphagnum peatlands, and the potential effects of the increasing atmospheric C02 concentration on these communities are not known. We analyzed the structure of microbial communities in five Sphagnum-dominated peatlands across Europe and their response to C02 enrichment using miniFACE systems. After three growing seasons, Sphagnum samples were analyzed for heterotrophic bacteria, cyanobacteria, microalgae, heterotrophic flagellates, ciliates, testate amoebae, fungi, nematodes, and rotifers. Heterotrophic organisms dominated the microbial communities and together represented 78% to 97% of the total microbial biomass. Testate amoebae dominated the protozoan biomass. A canonical correspondence analysis revealed a significant correlation between the microbial community data and four environmental variables (Na+, DOC, water table depth, and DIN), reflecting continentality, hydrology, and nitrogen deposition gradients. Carbon dioxide enrichment modified the structure of microbial communities, but total microbial biomass was unaffected. The biomass of heterotrophic bacteria increased by 48%, and the biomass of testate amoebae decreased by 13%. These results contrast with the absence of overall effect on methane production or on the vegetation, but are in line with an increased below-ground vascular plant biomass at the same sites. We interpret the increase in bacterial biomass as a response to a C02-induced enhancement of Sphagnum exudation. The causes for the decrease of testate amoebae are unclear but could indicate a top-down rather than a bottom-up control on their densit

Fractional Langevin equation

We investigate fractional Brownian motion with a microscopic random-matrix model and introduce a fractional Langevin equation. We use the latter to study both sub- and superdiffusion of a free particle coupled to a fractal heat bath. We further compare fractional Brownian motion with the fractal time process. The respective mean-square displacements of these two forms of anomalous diffusion exhibit the same power-law behavior. Here we show that their lowest moments are actually all identical, except the second moment of the velocity. This provides a simple criterion which enables to distinguish these two non-Markovian processes.Comment: 4 page

Consensus formation on adaptive networks

The structure of a network can significantly influence the properties of the dynamical processes which take place on them. While many studies have been devoted to this influence, much less attention has been devoted to the interplay and feedback mechanisms between dynamical processes and network topology on adaptive networks. Adaptive rewiring of links can happen in real life systems such as acquaintance networks where people are more likely to maintain a social connection if their views and values are similar. In our study, we consider different variants of a model for consensus formation. Our investigations reveal that the adaptation of the network topology fosters cluster formation by enhancing communication between agents of similar opinion, though it also promotes the division of these clusters. The temporal behavior is also strongly affected by adaptivity: while, on static networks, it is influenced by percolation properties, on adaptive networks, both the early and late time evolution of the system are determined by the rewiring process. The investigation of a variant of the model reveals that the scenarios of transitions between consensus and polarized states are more robust on adaptive networks.Comment: 11 pages, 14 figure

Restoring soil functionality in degraded areas of organic vineyards - Preliminary results of the ReSolVe project in the French vineyards

Degraded soil areas in vineyards are associated with problems in vine health, grape production and quality. Different causes for soil degradation are possible such as poor organic matter content, lower plant nutrient availability, pH, water deficiency, soil compaction / lower oxygenation… The aim of this preliminary study is to assess soil functionality (OM decomposition), biodiversity through mesofauna diversity and consequences for vine growth and quality