M-dwarf stellar winds: the effects of realistic magnetic geometry on rotational evolution and planets

We perform three-dimensional numerical simulations of stellar winds of early-M dwarf stars. Our simulations incorporate observationally reconstructed large-scale surface magnetic maps, suggesting that the complexity of the magnetic field can play an important role in the angular momentum evolution of the star, possibly explaining the large distribution of periods in field dM stars, as reported in recent works. In spite of the diversity of the magnetic field topologies among the stars in our sample, we find that stellar wind flowing near the (rotational) equatorial plane carries most of the stellar angular momentum, but there is no preferred colatitude contributing to mass loss, as the mass flux is maximum at different colatitudes for different stars. We find that more non-axisymmetric magnetic fields result in more asymmetric mass fluxes and wind total pressures $p_{\rm tot}$ (defined as the sum of thermal, magnetic and ram pressures). Because planetary magnetospheric sizes are set by pressure equilibrium between the planet's magnetic field and $p_{\rm tot}$, variations of up to a factor of $3$ in $p_{\rm tot}$ (as found in the case of a planet orbiting at several stellar radii away from the star) lead to variations in magnetospheric radii of about 20 percent along the planetary orbital path. In analogy to the flux of cosmic rays that impact the Earth, which is inversely modulated with the non-axisymmetric component of the total open solar magnetic flux, we conclude that planets orbiting M dwarf stars like DT~Vir, DS~Leo and GJ~182, which have significant non-axisymmetric field components, should be the more efficiently shielded from galactic cosmic rays, even if the planets lack a protective thick atmosphere/large magnetosphere of their own.Comment: 16 pages, 9 figures, to appear in MNRA

Modeling the RV jitter of early M dwarfs using tomographic imaging

In this paper we show how tomographic imaging (Zeeman Doppler Imaging, ZDI) can be used to characterize stellar activity and magnetic field topologies, ultimately allowing to filter out the radial velocity (RV) activity jitter of M-dwarf moderate rotators. This work is based on spectropolarimetric observations of a sample of five weakly-active early M-dwarfs (GJ 205, GJ 358, GJ 410, GJ479, GJ 846) with HARPS-Pol and NARVAL. These stars have v sin i and RV jitters in the range 1-2 km/s and 2.7-10.0 m/s rms respectively. Using a modified version of ZDI applied to sets of phase-resolved Least-Squares- Decon- volved (LSD) profiles of unpolarized spectral lines, we are able to characterize the distribution of active regions at the stellar surfaces. We find that darks spots cover less than 2% of the total surface of the stars of our sample. Our technique is e cient at modeling the rotationally mod- ulated component of the activity jitter, and succeeds at decreasing the amplitude of this com- ponent by typical factors of 2-3 and up to 6 in optimal cases. From the rotationally modulated time-series of circularly polarized spectra and with ZDI, we also reconstruct the large-scale magnetic field topology. These fields suggest that bi-stability of dynamo processes observed in active M dwarfs may also be at work for moderately active M dwarfs. Comparing spot distributions with field topologies suggest that dark spots causing activity jitter concentrate at the magnetic pole and/or equator, to be confirmed with future data on a larger sample.Comment: 34 pages, accepted for publication in MNRA

Powerful Winds from Low-Mass Stars: V374 Peg

The rapid rotation (P=0.44 d) of the M dwarf V374Peg (M4) along with its intense magnetic field point toward magneto-centrifugal acceleration of a coronal wind. In this work, we investigate the structure of the wind of V374Peg by means of 3D magnetohydrodynamical (MHD) numerical simulations. For the first time, an observationally derived surface magnetic field map is implemented in MHD models of stellar winds for a low mass star. We show that the wind of V374Peg deviates greatly from a low-velocity, low-mass-loss rate solar-type wind. We find general scaling relations for the terminal velocities, mass-loss rates, and spin-down times of highly magnetized M dwarfs. In particular, for V374Peg, our models show that terminal velocities across a range of stellar latitudes reach ~(1500-2300) n_{12}^{-1/2} km/s, where n_{12} is the coronal wind base density in units of 10^{12} cm^{-3}, while the mass-loss rates are about 4 x 10^{-10} n_{12}^{1/2} Msun/yr. We also evaluate the angular-momentum loss of V374Peg, which presents a rotational braking timescale ~28 n_{12}^{-1/2} Myr. Compared to observationally derived values from period distributions of stars in open clusters, this suggests that V374Peg may have low coronal base densities (< 10^{11} cm^{-3}). We show that the wind ram pressure of V374Peg is about 5 orders of magnitude larger than for the solar wind. Nevertheless, a small planetary magnetic field intensity (~ 0.1G) is able to shield a planet orbiting at 1 AU against the erosive effects of the stellar wind. However, planets orbiting inside the habitable zone of V374Peg, where the wind ram pressure is higher, might be facing a more significant atmospheric erosion. In that case, higher planetary magnetic fields of, at least, about half the magnetic field intensity of Jupiter, are required to protect the planet's atmosphere.Comment: 13 pages, 5 figures, 1 table. MNRAS in pres

A common mechanism of defective channel trafficking underlying DFNA2 hearing loss result in different cell surface expression levels of KCNQ4 mutants

KCNQ4 mutations underlie DFNA2, a subtype of autosomal dominant hearing loss. We had previously identified the pore-region p.G296S mutation that impaired channel activity in two manners: it greatly reduced surface expression and abolished channel function. Moreover, G296S mutant exerted a strong dominant-negative effect on potassium currents by reducing the channel expression at the cell surface representing the first study to identify a trafficking-dependent dominant mechanism for the loss of KCNQ4 channel function in DFNA2. Here, we have investigated the pathogenic mechanism associated with all the described KCNQ4 mutations (F182L, W242X, E260K, D262V, L274H, W276S, L281S, G285C, G285S and G321S) that are located in different domains of the channel protein. F182L mutant showed a wild type-like cell-surface distribution in transiently transfected NIH3T3 fibroblasts and the recorded currents in Xenopus oocytes resembled those of the wild-type. The remaining KCNQ4 mutants abolished potassium currents, but displayed distinct levels of defective cell-surface expression in NIH3T3 as quantified by flow citometry. Co-localization studies revealed these mutants were retained in the ER, unless W242X, which showed a clear co-localization with Golgi apparatus. Interestingly, this mutation results in a truncated KCNQ4 protein at the S5 transmembrane domain, before the pore region, that escapes the protein quality control in the ER but does not reach the cell surface at normal levels. Currently we are investigating the trafficking behaviour and electrophysiological properties of several KCNQ4 truncated proteins artificially generated in order to identify specific motifs involved in channel retention/exportation. Altogether, our results indicate that a defect in KCNQ4 trafficking is the common mechanism underlying DFNA

Convergence of simple adaptive Galerkin schemes based on h − h/2 error estimators

We discuss several adaptive mesh-refinement strategies based on (h − h/2)-error estimation. This class of adaptivemethods is particularly popular in practise since it is problem independent and requires virtually no implementational overhead. We prove that, under the saturation assumption, these adaptive algorithms are convergent. Our framework applies not only to finite element methods, but also yields a first convergence proof for adaptive boundary element schemes. For a finite element model problem, we extend the proposed adaptive scheme and prove convergence even if the saturation assumption fails to hold in general

Social representations of HIV/AIDS in five Central European and Eastern European countries: A multidimensional analysis

Cognitive processing models of risky sexual behaviour have proliferated in the two decades since the first reporting of HIV/AIDS, but far less attention has been paid to individual and group representations of the epidemic and the relationship between these representations and reported sexual behaviours. In this study, 494 business people and medics from Estonia, Georgia, Hungary, Poland and Russia sorted free associations around HIV/AIDS in a matrix completion task. Exploratory factor and multidimensional scaling analyses revealed two main dimensions (labelled ‘Sex’ and ‘Deadly disease’), with significant cultural and gender variations along both dimension scores. Possible explanations for these results are discussed in the light of growing concerns over the spread of the epidemic in this region

DeepWalk: Online Learning of Social Representations

We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide $F_1$ scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection.Comment: 10 pages, 5 figures, 4 table