Higgs-μ\mu-τ\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)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