Constraint on the branching ratio of B_c \to tau nu from LEP1 and consequences for R(D(*)) anomaly

Recently there has been interest in the correlation between R(D*) and the branching ratio (BR) of $B_c \to \tau \nu$ in models with a charged scalar H^\pm. Any enhancement of R(D*) by $H^\pm$ alone (in order to agree with current data) also enhances $BR(B_c \to \tau \nu$), for which there has been no direct search at hadron colliders. We show that LEP data taken at the Z peak requires BR($B_c \to \tau \nu$) < 10%, and this constraint is significantly stronger than the recent constraint BR($B_c \to \tau \nu$) < 30% from considering the lifetime of B_c. In order to respect this new constraint, any explanation of the R(D) and R(D*) anomaly in terms of $H^\pm$ alone would require the future measurements of R(D*) to be even closer to the Standard Model prediction. A stronger limit on BR($B_c \to \tau \nu$) (or its first measurement) would be obtained if the L3 collaboration used all its data taken at the Z peak.Comment: 14 pages, 4 figures, a reference and two sentences adde

Distributed H-infinity filtering for polynomial nonlinear stochastic systems in sensor networks

Copyright [2010] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this paper, the distributed H1 filtering problem is addressed for a class of polynomial nonlinear stochastic systems in sensor networks. For a Lyapunov function candidate whose entries are polynomials, we calculate its first- and second-order derivatives in order to facilitate the use of Itos differential role. Then, a sufficient condition for the existence of a feasible solution to the addressed distributed H1 filtering problem is derived in terms of parameter-dependent linear matrix inequalities (PDLMIs). For computational convenience, these PDLMIs are further converted into a set of sums of squares (SOSs) that can be solved effectively by using the semidefinite programming technique. Finally, a numerical simulation example is provided to demonstrate the effectiveness and applicability of the proposed design approach.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., the National 973 Program of China under Grant 2009CB320600, the National Natural Science Foundation of China under Grant 60974030 and the Alexander von Humboldt Foundation of Germany