6,887 research outputs found

### Higgs-$\mu$-$\tau$ Coupling at High and Low Energy Colliders

There is no tree-level flavor changing neutral current (FCNC) in the standard
model (SM) which contains only one Higgs doublet. If more Higgs doublets are
introduced for various reasons, the tree level FCNC would be inevitable except
extra symmetry was imposed. Therefore FCNC processes are the excellent probes
for the physics beyond the SM (BSM). In this paper, we studied the lepton
flavor violated (LFV) decay processes $h\rightarrow\mu\tau$ and
$\tau\rightarrow\mu\gamma$ induced by Higgs-$\mu$-$\tau$ vertex. For
$\tau\rightarrow\mu\gamma$, its branching ratio is also related to the
$ht\bar{t}$, $h\tau^+\tau^-$ and $hW^+W^-$ vertices. We categorized the BSM
into two scenarios for the Higgs coupling strengths near or away from SM. For
the latter scenario, we took the spontaneously broken two Higgs doublet model
(Lee model) as an example. We considered the constraints by recent data from
LHC and B factories, and found that the measurements gave weak constraints. At
LHC Run II, $h\rightarrow\mu\tau$ will be confirmed or set stricter limit on
its branching ratio. Accordingly,
$\textrm{Br}(\tau\rightarrow\mu\gamma)\lesssim\mathcal{O}(10^{-10}-10^{-8})$
for general chosen parameters. For the positive case,
$\tau\rightarrow\mu\gamma$ can be discovered with $\mathcal{O}(10^{10})$ $\tau$
pair samples at SuperB factory, Super $\tau$-charm factory and new Z-factory.
The future measurements for $\textrm{Br}(h\rightarrow\mu\tau)$ and
$\textrm{Br}(\tau\rightarrow\mu\gamma)$ will be used to distinguish these two
scenarios or set strict constraints on the correlations among different Higgs
couplings, please see Table II in the text for details.Comment: 18 pages, 10 figures, 2 table; more references added; more
discussions about cancellation in the amplitude added accoeding to the
referee's suggestion

### Testing the phenomenological interacting dark energy with observational $H(z)$ data

In order to test the possible interaction between dark energy and dark
matter, we investigate observational constraints on a phenomenological
scenario, in which the ratio between the dark energy and matter densities is
proportional to the power law case of the scale factor, $r\equiv
(\rho_X/\rho_m)\propto a^{\xi}$. By using the Markov chain Monte Carlo method,
we constrain the phenomenological interacting dark energy model with the newly
revised $H(z)$ data, as well as the cosmic microwave background (CMB)
observation from the 7-year Wilkinson Microwave Anisotropy Probe (WMAP7)
results, the baryonic acoustic oscillation (BAO) observation from the
spectroscopic Sloan Digital Sky Survey (SDSS) data release 7 (DR7) galaxy
sample and the type Ia supernovae (SNe Ia) from Union2 set. The best-fit values
of the model parameters are
$\Omega_{m0}=0.27_{-0.02}^{+0.02}(1\sigma)_{-0.03}^{+0.04}(2\sigma)$,
$\xi=3.15_{-0.50}^{+0.48}(1\sigma)_{-0.71}^{+0.72}(2\sigma)$, and
$w_X=-1.05_{-0.14}^{+0.15}(1\sigma)_{-0.21}^{+0.21}(2\sigma)$, which are more
stringent than previous results. These results show that the standard
$\Lambda$CDM model without any interaction remains a good fit to the recent
observational data; however, the interaction that the energy transferring from
dark matter to dark energy is slightly favored over the interaction from dark
energy to dark matter. It is also shown that the $H(z)$ data can give more
stringent constraints on the phenomenological interacting scenario when
combined to CMB and BAO observations, and the confidence regions of
$H(z)$+BAO+CMB, SNe+BAO+CMB, and $H(z)$+SNe+BAO+CMB combinations are consistent
with each other.Comment: 6 pages, 4 figures, 1 table. MNRAS in pres

### Random lasso

We propose a computationally intensive method, the random lasso method, for
variable selection in linear models. The method consists of two major steps. In
step 1, the lasso method is applied to many bootstrap samples, each using a set
of randomly selected covariates. A measure of importance is yielded from this
step for each covariate. In step 2, a similar procedure to the first step is
implemented with the exception that for each bootstrap sample, a subset of
covariates is randomly selected with unequal selection probabilities determined
by the covariates' importance. Adaptive lasso may be used in the second step
with weights determined by the importance measures. The final set of covariates
and their coefficients are determined by averaging bootstrap results obtained
from step 2. The proposed method alleviates some of the limitations of lasso,
elastic-net and related methods noted especially in the context of microarray
data analysis: it tends to remove highly correlated variables altogether or
select them all, and maintains maximal flexibility in estimating their
coefficients, particularly with different signs; the number of selected
variables is no longer limited by the sample size; and the resulting prediction
accuracy is competitive or superior compared to the alternatives. We illustrate
the proposed method by extensive simulation studies. The proposed method is
also applied to a Glioblastoma microarray data analysis.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS377 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org

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