A second Higgs from the Higgs portal

In the Higgs portal framework, the Higgs field generally mixes with the Standard Model singlet leading to the existence of two states, one of which is identified with the 125 GeV scalar observed at the LHC. In this work, we analyse direct and indirect constraints on the second mass eigenstate and the corresponding mixing angle. The existence of the additional scalar can be beneficial as it can stabilise the otherwise-metastable electroweak vacuum. We find parameter regions where all of the bounds, including the stability constraints, are satisfied. We also study prospects for observing the decay of the heavier state into a pair of the 125 GeV Higgs-like scalars.Comment: 16 pages, updated figures 3 and 4 with new limits from Higgs-searches at the LHC, minor text adjustments, references adde

Spatial motion of the Magellanic Clouds. Tidal models ruled out?

Recently, Kallivayalil et al. derived new values of the proper motion for the Large and Small Magellanic Clouds (LMC and SMC, respectively). The spatial velocities of both Clouds are unexpectedly higher than their previous values resulting from agreement between the available theoretical models of the Magellanic System and the observations of neutral hydrogen (HI) associated with the LMC and the SMC. Such proper motion estimates are likely to be at odds with the scenarios for creation of the large-scale structures in the Magellanic System suggested so far. We investigated this hypothesis for the pure tidal models, as they were the first ones devised to explain the evolution of the Magellanic System, and the tidal stripping is intrinsically involved in every model assuming the gravitational interaction. The parameter space for the Milky Way (MW)-LMC-SMC interaction was analyzed by a robust search algorithm (genetic algorithm) combined with a fast restricted N-body model of the interaction. Our method extended the known variety of evolutionary scenarios satisfying the observed kinematics and morphology of the Magellanic large-scale structures. Nevertheless, assuming the tidal interaction, no satisfactory reproduction of the HI data available for the Magellanic Clouds was achieved with the new proper motions. We conclude that for the proper motion data by Kallivayalil et al., within their 1-sigma errors, the dynamical evolution of the Magellanic System with the currently accepted total mass of the MW cannot be explained in the framework of pure tidal models. The optimal value for the western component of the LMC proper motion was found to be pm_w(LMC) > -1.3 mas/yr in case of tidal models. It corresponds to the reduction of the Kallivayalil et al. value for pm_w(LMC) by approx. 40% in its magnitude.Comment: ApJ accepted, 17 pages, 4 figure

The long and short of it: Global liberalization, poverty and inequality

Global deregulation of current and capital account is often touted as successful means to reduce poverty and inequality. On the face of it, though, the evidence does not support this claim. Rising intra-country inequality is widespread, income inequality between countries grows, the absolute number of people living in poverty increases, and poverty rate reductions are geographically isolated. Critics of global deregulation have charged that more deregulated trade flows result in a worse income distribution and unregulated capital flows in more macro economic instabilities that are especially harmful to the poor. Using data from the World Bank, the IMF and the UN, we test the impact of increased deregulation on the incomes of the poor. Our results indicate that global deregulation of trade and capital markets does hurt the poor. We find that the income share of the poor is generally lower in deregulated and in macro economically less stable environments, which are more prone to occur after capital account liberalization. The evidence also suggests that trade flows in more regulated environments may be good for growth and, by extension, for the poor in the long-run. --

Private Graphon Estimation for Sparse Graphs

We design algorithms for fitting a high-dimensional statistical model to a large, sparse network without revealing sensitive information of individual members. Given a sparse input graph $G$, our algorithms output a node-differentially-private nonparametric block model approximation. By node-differentially-private, we mean that our output hides the insertion or removal of a vertex and all its adjacent edges. If $G$ is an instance of the network obtained from a generative nonparametric model defined in terms of a graphon $W$, our model guarantees consistency, in the sense that as the number of vertices tends to infinity, the output of our algorithm converges to $W$ in an appropriate version of the $L_2$ norm. In particular, this means we can estimate the sizes of all multi-way cuts in $G$. Our results hold as long as $W$ is bounded, the average degree of $G$ grows at least like the log of the number of vertices, and the number of blocks goes to infinity at an appropriate rate. We give explicit error bounds in terms of the parameters of the model; in several settings, our bounds improve on or match known nonprivate results.Comment: 36 page