arXiv subjects
Publications and source records attributed to Till Moritz Karbach.
I report on an updated combination of all currently available tree-level measurements of the CKM angle $γ$ from LHCb. This combination currently gives LHCb's most precise value of $γ= (73^{+9}_{-10})^\circ$ obtained from $B\to DK$-like decays. For the first time in a single experiment, the uncertainty has dropped below $10^\circ$. When using $B\to Dπ$ decays in addition, the situation becomes more difficult to interpret.
In neutral meson mixing, a certain class of convolution integrals is required whose solution involves the error function $\mathrm{erf}(z)$ of a complex argument $z$. We show the the general shape of the analytic solution of these integrals, and give expressions which allow the normalisation of these expressions for use in probability density functions. Furthermore, we derive expressions which allow a (decay time) acceptance to be included in these integrals, or allow the calculation of moments. We also describe the implementation of numerical routines which allow the numerical evaluation of $w(z)=e^{-z^2}(1-\mathrm{erf}(-iz))$, sometimes also called Faddeeva function, in C++. These new routines improve over the old CERNLIB routine(s) WWERF/CWERF in terms of both speed and accuracy. These new routines are part of the RooFit package, and have been distributed with it since ROOT version 5.34/08.
I present a measurement of the CKM angle gamma from a combination of three LHCb measurements using the tree decays B -> DK and B -> Dpi. These measurements are based on a dataset corresponding to 1.0fb-1, collected in 2011. In contrast to what was presented at FPCP, these proceedings fully include the effect of D0--D0b mixing on the combination. I also report on the inclusion of new, preliminary results on the Dalitz analysis of B->DK, D->KShh, using a dataset corresponding to 3fb-1, collected in 2011--2012.
The method of extended maximum likelihood is a well known concept of parameter estimation. One can implement external knowledge on the unknown parameters by multiplying the likelihood by constraint terms. In this note, we emphasize that this is also true for yield parameters in an extended maximum likelihood fit, which is widely used in the particle physics community. We recommend a way to generate pseudo-experiments in presence of constraint terms on yield parameters, and point to pitfalls inside the RooFit framework.
I present three studies from the LHCb experiment on the subject of direct CP violation in $B^0$ and $B_s$ decays. First, we measure the CP asymmetry in $B^\pm\toψK^\pm$ decays, with $ψ=J/ψ, ψ(2S)$, using 0.35 $fb^{-1}$ of $pp$ collisions at $\sqrt{s}=7 TeV$. We find no evidence for CP violation. Second, using the same data sample, we see the first evidence of CP violation in the decays of $B_s$ mesons to $K^\pmπ^-π^+$ pairs, $A_{CP}(B_s\to Kπ) = 0.27 \pm 0.08 {\rm (stat)}\pm 0.02 {\rm (syst)}$ ($3.3σ$). Third, using 1.0 $fb^{-1}$ of data, measurements of CP sensitive observables of the $B^\pm\to D K^\pm$ system are presented. They include the first observation of the suppressed mode $B^\pm\to[π^\pm K^\mp]_D K^\pm$. Combining several $D$ final states, CP violation in $B^\pm\to D K^\pm$ decays is observed with a significance of $5.8σ$.
We present studies from the LHCb experiment leading to the measurement of the weak phase ϕs. At first, flavor tagging is established by measuring the B0s oscillation frequency \Deltams. Then, flavor tagging is used to perform a measurement of the well known CKM angle sin 2β in B0 \rightarrow J/ψ KS0, before we constrain ϕs through an amplitude analysis of B0s \rightarrow J/ψ ϕ decays. These studies use about 35 inverse pb of data taken in 2010. In addition, we present the measurement of B(B+\rightarrowJ/ψπ+)/B(B+\rightarrowJ/ψK+) and the first observation of B0s \rightarrowJ/ψf2'(1525).
In particle physics, the likelihood ratio ordering principle is frequently used to determine confidence regions. This method has statistical properties that are superior to that of other confidence regions. But it often requires intensive computations involving thousands of toy Monte Carlo datasets. The original paper by Feldman and Cousins contains a recipe to perform the toy MC computation. In this note, we explain their recipe in a more algorithmic way, show its connection to 1-CL plots, and apply it to simple Gaussian situations with boundaries.