7,596 research outputs found
Fourth generation Majorana neutrino, dark matter and Higgs physics
We consider extensions of the standard model with fourth generation fermions
(SM4) in which extra symmetries are introduced such that the transitions
between the fourth generation fermions and the ones in the first three
generations are forbidden. In these models, the stringent lower bounds on the
masses of fourth generation quarks from direct searches are relaxed, and the
lightest fourth neutrino is allowed to be stable and light enough to trigger
the Higgs boson invisible decay. In addition, the fourth Majorana neutrino can
be a subdominant but highly detectable dark matter component. We perform a
global analysis of the current LHC data on the Higgs production and decay in
this type of SM4. The results show that the mass of the lightest fourth
Majorana neutrino is confined in the range GeV. Within the allowed
parameter space, the predicted effective cross-section for spin-independent
DM-nucleus scattering is , which is close to the current Xenon100 upper limit and is
within the reach of the Xenon1T experiment in the near future. The predicted
spin-dependent cross sections can also reach .Comment: arXiv admin note: substantial text overlap with arXiv:1110.293
Quantum Dimensionality Reduction by Linear Discriminant Analysis
Dimensionality reduction (DR) of data is a crucial issue for many machine
learning tasks, such as pattern recognition and data classification. In this
paper, we present a quantum algorithm and a quantum circuit to efficiently
perform linear discriminant analysis (LDA) for dimensionality reduction.
Firstly, the presented algorithm improves the existing quantum LDA algorithm to
avoid the error caused by the irreversibility of the between-class scatter
matrix in the original algorithm. Secondly, a quantum algorithm and
quantum circuits are proposed to obtain the target state corresponding to the
low-dimensional data. Compared with the best-known classical algorithm, the
quantum linear discriminant analysis dimensionality reduction (QLDADR)
algorithm has exponential acceleration on the number of vectors and a
quadratic speedup on the dimensionality of the original data space, when
the original dataset is projected onto a polylogarithmic low-dimensional space.
Moreover, the target state obtained by our algorithm can be used as a submodule
of other quantum machine learning tasks. It has practical application value of
make that free from the disaster of dimensionality
Width-tuned magnetic order oscillation on zigzag edges of honeycomb nanoribbons
Quantum confinement and interference often generate exotic properties in
nanostructures. One recent highlight is the experimental indication of a
magnetic phase transition in zigzag-edged graphene nanoribbons at the critical
ribbon width of about 7 nm [G. Z. Magda et al., Nature \textbf{514}, 608
(2014)]. Here we show theoretically that with further increase in the ribbon
width, the magnetic correlation of the two edges can exhibit an intriguing
oscillatory behavior between antiferromagnetic and ferromagnetic, driven by
acquiring the positive coherence between the two edges to lower the free
energy. The oscillation effect is readily tunable in applied magnetic fields.
These novel properties suggest new experimental manifestation of the edge
magnetic orders in graphene nanoribbons, and enhance the hopes of graphene-like
spintronic nanodevices functioning at room temperature.Comment: 22 pages, 9 figure
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