Kinetic simulation of neutral∕ionized gas and electrically charged dust in the coma of comet 67P∕Churyumov-Gerasimenko

The cometary coma is a unique phenomenon in the solar system being a planetary atmosphere influenced by little or no gravity. As a comet approaches the sun, the water vapor with some fraction of other gases sublimate, generating a cloud of gas, ice and other refractory materials (rocky and organic dust) ejected from the surface of the nucleus. Sublimating gas molecules undergo frequent collisions and photochemical processes in the near‐nucleus region. Owing to its negligible gravity, comets produce a large and highly variable extensive dusty coma with a size much larger than the characteristic size of the cometary nucleus. The Rosetta spacecraft is en route to comet 67P/Churyumov‐Gerasimenko for a rendezvous, landing, and extensive orbital phase beginning in 2014. Both, interpretation of measurements and safety consideration of the spacecraft require modeling of the comet’s dusty gas environment. In this work we present results of a numerical study of multispecies gaseous and electrically charged dust environment of comet Chyuryumov‐Gerasimenko. Both, gas and dust phases of the coma are simulated kinetically. Photolytic reactions are taken into account. Parameters of the ambient plasma as well as the distribution of electric/magnetic fields are obtained from an MHD simulation [1] of the coma connected to the solar wind. Trajectories of ions and electrically charged dust grains are simulated by accounting for the Lorentz force and the nucleus gravity.

Comet 1P/Halley multifluid MHD model for the Giotto fly-by

The interaction of comets with the solar wind has been the focus of many studies including numerical modeling. We compare the results of our multifluid MHD simulation of comet 1P/Halley to data obtained during the flyby of the European Space Agency's Giotto spacecraft in 1986. The model solves the full set of MHD equations for the individual fluids representing the solar wind protons, the cometary light and heavy ions, and the electrons. The mass loading, charge-exchange, dissociative ion-electron recombination, and collisional interactions between the fluids are taken into account. The computational domain spans over several million kilometers, and the close vicinity of the comet is resolved to the details of the magnetic cavity. The model is validated by comparison to the corresponding Giotto observations obtained by the Ion Mass Spectrometer, the Neutral Mass Spectrometer, the Giotto magnetometer experiment, and the Johnstone Plasma Analyzer instrument. The model shows the formation of the bow shock, the ion pile-up, and the diamagnetic cavity and is able to reproduce the observed temperature differences between the pick-up ion populations and the solar wind protons. We give an overview of the global interaction of the comet with the solar wind and then show the effects of the Lorentz force interaction between the different plasma populations.

fMRI-Compatible Electromagnetic Haptic Interface

A new haptic interface device is suggested, which can be used for functional magnetic resonance imaging (fMRI) studies. The basic component of this 1 DOF haptic device are two coils that produce a Lorentz force induced by the large static magnetic field of the MR scanner. A MR-compatible optical angular encoder and a optical force sensor enable the implementation of different control architectures for haptic interactions. The challenge was to provide a large torque, and not to affect image quality by the currents applied in the device. The haptic device was tested in a 3T MR scanner. With a current of up to 1A and a distance of 1m to the focal point of the MR-scanner it was possible to generate torques of up to 4 Nm. Within these boundaries image quality was not affected.

Nonparametric location estimators in the randomized complete block design

Several tests for the comparison of different groups in the randomized complete block design exist. However, there is a lack of robust estimators for the location difference between one group and all the others on the original scale. The relative marginal effects are commonly used in this situation, but they are more difficult to interpret and use by less experienced people because of the different scale. In this paper two nonparametric estimators for the comparison of one group against the others in the randomized complete block design will be presented. Theoretical results such as asymptotic normality, consistency, translation invariance, scale preservation, unbiasedness, and median unbiasedness are derived. The finite sample behavior of these estimators is derived by simulations of different scenarios. In addition, possible confidence intervals with these estimators are discussed and their behavior derived also by simulations.

Hadronic light-by-light scattering and the muon g−2

The largest uncertainties in the Standard Model calculation of the anomalous magnetic moment of the muon (g − 2)μ come from hadronic contributions. In particular, it can be expected that in a few years the subleading hadronic light-by-light (HLbL) contribution will dominate the theory uncertainty. We present a dispersive description of the HLbL tensor, which is based on unitarity, analyticity, crossing symmetry, and gauge invariance. Such a model-independent Approach opens up an avenue towards a data-driven determination of the HLbL contribution to the (g − 2)μ.

Finite volume effects in chiral perturbation theory with twisted boundary conditions

We study the effects of a finite cubic volume with twisted boundary conditions on pseudoscalar mesons. We apply Chiral Perturbation Theory in the p-regime and introduce the twist by means of a constant vector field. The corrections of masses, decay constants, pseudoscalar coupling constants and form factors are calculated at next-to-leading order. We detail the derivations and compare with results available in the literature. In some case there is disagreement due to a different treatment of new extra terms generated from the breaking of the cubic invariance. We advocate to treat such terms as renormalization terms of the twisting angles and reabsorb them in the on-shell conditions. We confirm that the corrections of masses, decay constants, pseudoscalar coupling constants are related by means of chiral Ward identities. Furthermore, we show that the matrix elements of the scalar (resp. vector) form factor satisfies the Feynman–Hellman Theorem (resp. the Ward–Takahashi identity). To show the Ward–Takahashi identity we construct an effective field theory for charged pions which is invariant under electromagnetic gauge transformations and which reproduces the results obtained with Chiral Perturbation Theory at a vanishing momentum transfer. This generalizes considerations previously published for periodic boundary conditions to twisted boundary conditions. Another method to estimate the corrections in finite volume are asymptotic formulae. Asymptotic formulae were introduced by Lüscher and relate the corrections of a given physical quantity to an integral of a specific amplitude, evaluated in infinite volume. Here, we revise the original derivation of Lüscher and generalize it to finite volume with twisted boundary conditions. In some cases, the derivation involves complications due to extra terms generated from the breaking of the cubic invariance. We isolate such terms and treat them as renormalization terms just as done before. In that way, we derive asymptotic formulae for masses, decay constants, pseudoscalar coupling constants and scalar form factors. At the same time, we derive also asymptotic formulae for renormalization terms. We apply all these formulae in combination with Chiral Perturbation Theory and estimate the corrections beyond next-to-leading order. We show that asymptotic formulae for masses, decay constants, pseudoscalar coupling constants are related by means of chiral Ward identities. A similar relation connects in an independent way asymptotic formulae for renormalization terms. We check these relations for charged pions through a direct calculation. To conclude, a numerical analysis quantifies the importance of finite volume corrections at next-to-leading order and beyond. We perform a generic Analysis and illustrate two possible applications to real simulations.

MOREMATA: Stata module (Mata) to provide various functions

This package includes various Mata functions. kern(): various kernel functions; kint(): kernel integral functions; kdel0(): canonical bandwidth of kernel; quantile(): quantile function; median(): median; iqrange(): inter-quartile range; ecdf(): cumulative distribution function; relrank(): grade transformation; ranks(): ranks/cumulative frequencies; freq(): compute frequency counts; histogram(): produce histogram data; mgof(): multinomial goodness-of-fit tests; collapse(): summary statistics by subgroups; _collapse(): summary statistics by subgroups; gini(): Gini coefficient; sample(): draw random sample; srswr(): SRS with replacement; srswor(): SRS without replacement; upswr(): UPS with replacement; upswor(): UPS without replacement; bs(): bootstrap estimation; bs2(): bootstrap estimation; bs_report(): report bootstrap results; jk(): jackknife estimation; jk_report(): report jackknife results; subset(): obtain subsets, one at a time; composition(): obtain compositions, one by one; ncompositions(): determine number of compositions; partition(): obtain partitions, one at a time; npartitionss(): determine number of partitions; rsubset(): draw random subset; rcomposition(): draw random composition; colvar(): variance, by column; meancolvar(): mean and variance, by column; variance0(): population variance; meanvariance0(): mean and population variance; mse(): mean squared error; colmse(): mean squared error, by column; sse(): sum of squared errors; colsse(): sum of squared errors, by column; benford(): Benford distribution; cauchy(): cumulative Cauchy-Lorentz dist.; cauchyden(): Cauchy-Lorentz density; cauchytail(): reverse cumulative Cauchy-Lorentz; invcauchy(): inverse cumulative Cauchy-Lorentz; rbinomial(): generate binomial random numbers; cebinomial(): cond. expect. of binomial r.v.; root(): Brent's univariate zero finder; nrroot(): Newton-Raphson zero finder; finvert(): univariate function inverter; integrate_sr(): univariate function integration (Simpson's rule); integrate_38(): univariate function integration (Simpson's 3/8 rule); ipolate(): linear interpolation; polint(): polynomial inter-/extrapolation; plot(): Draw twoway plot; _plot(): Draw twoway plot; panels(): identify nested panel structure; _panels(): identify panel sizes; npanels(): identify number of panels; nunique(): count number of distinct values; nuniqrows(): count number of unique rows; isconstant(): whether matrix is constant; nobs(): number of observations; colrunsum(): running sum of each column; linbin(): linear binning; fastlinbin(): fast linear binning; exactbin(): exact binning; makegrid(): equally spaced grid points; cut(): categorize data vector; posof(): find element in vector; which(): positions of nonzero elements; locate(): search an ordered vector; hunt(): consecutive search; cond(): matrix conditional operator; expand(): duplicate single rows/columns; _expand(): duplicate rows/columns in place; repeat(): duplicate contents as a whole; _repeat(): duplicate contents in place; unorder2(): stable version of unorder(); jumble2(): stable version of jumble(); _jumble2(): stable version of _jumble(); pieces(): break string into pieces; npieces(): count number of pieces; _npieces(): count number of pieces; invtokens(): reverse of tokens(); realofstr(): convert string into real; strexpand(): expand string argument; matlist(): display a (real) matrix; insheet(): read spreadsheet file; infile(): read free-format file; outsheet(): write spreadsheet file; callf(): pass optional args to function; callf_setup(): setup for mm_callf().