10,145 research outputs found
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 , 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 is an instance of the
network obtained from a generative nonparametric model defined in terms of a
graphon , our model guarantees consistency, in the sense that as the number
of vertices tends to infinity, the output of our algorithm converges to in
an appropriate version of the norm. In particular, this means we can
estimate the sizes of all multi-way cuts in .
Our results hold as long as is bounded, the average degree of 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
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