Particle Acceleration and Plasma Dynamics during Magnetic Reconnection in the Magnetically-dominated Regime

Magnetic reconnection is thought to be the driver for many explosive phenomena in the universe. The energy release and particle acceleration during reconnection have been proposed as a mechanism for producing high-energy emissions and cosmic rays. We carry out two- and three-dimensional kinetic simulations to investigate relativistic magnetic reconnection and the associated particle acceleration. The simulations focus on electron-positron plasmas starting with a magnetically dominated, force-free current sheet ($\sigma \equiv B^2/(4\pi n_e m_e c^2) \gg 1$). For this limit, we demonstrate that relativistic reconnection is highly efficient at accelerating particles through a first-order Fermi process accomplished by the curvature drift of particles along the electric field induced by the relativistic flows. This mechanism gives rise to the formation of hard power-law spectra $f \propto (\gamma-1)^{-p}$ and approaches $p = 1$ for sufficiently large $\sigma$ and system size. Eventually most of the available magnetic free energy is converted into nonthermal particle kinetic energy. An analytic model is presented to explain the key results and predict a general condition for the formation of power-law distributions. The development of reconnection in these regimes leads to relativistic inflow and outflow speeds and enhanced reconnection rates relative to non-relativistic regimes. In the three-dimensional simulation, the interplay between secondary kink and tearing instabilities leads to strong magnetic turbulence, but does not significantly change the energy conversion, reconnection rate, or particle acceleration. This study suggests that relativistic reconnection sites are strong sources of nonthermal particles, which may have important implications to a variety of high-energy astrophysical problems.Comment: 18 pages, 13 figures, slightly modified after submitted to Ap

The Matthew Effect and widely prescribed pharmaceuticals lacking environmental monitoring: Case study of an exposure-assessment vulnerability

AbstractAssessing ambient exposure to chemical stressors often begins with time-consuming and costly monitoring studies to establish environmental occurrence. Both human and ecological toxicology are currently challenged by the unknowns surrounding low-dose exposure/effects, compounded by the reality that exposure undoubtedly involves mixtures of multiple stressors whose identities and levels can vary over time. Long absent from the assessment process, however, is whether the full scope of the identities of the stressors is sufficiently known. The Matthew Effect (a psychosocial phenomenon sometimes informally called the “bandwagon effect” or “iceberg effect,” among others) may adversely bias or corrupt the exposure assessment process. The Matthew Effect is evidenced by decisions that base the selection of stressors to target in environmental monitoring surveys on whether they have been identified in prior studies, rather than considering the possibility that additional, but previously unreported, stressors might also play important roles in an exposure scenario. The possibility that the Matthew Effect might influence the scope of environmental stressor research is explored for the first time in a comprehensive case study that examines the preponderance of “absence of data” (in contrast to positive data and “data of absence”) for the environmental occurrence of a very large class of potential chemical stressors associated with ubiquitous consumer use — active pharmaceutical ingredients (APIs). Comprehensive examination of the published data for an array of several hundred of the most frequently used drugs for whether their APIs are environmental contaminants provides a prototype example to catalyze discussion among the many disciplines involved with assessing risk. The findings could help guide the selection of those APIs that might merit targeting for environmental monitoring (based on the absence of data for environmental occurrence) as well as the prescribing of those medications that might have minimal environmental impact (based on data of absence for environmental occurrence)