Advertisement

Use of \(\Lambda_b\) polarimetry in top quark spin-correlation functions

  • C.A. Nelson
Theoretical physics

Abstract.

Due to the absence of hadronization effects and the large \(m_t\) mass, top quark decay will be uniquely sensitive to fundamental electroweak physics at the Tevatron, at the LHC, and at a future linear collider. A “complete measurement” of the four helicity amplitudes in \(t \rightarrow W^+ b\) decay is possible by the combined use of \(\Lambda_b\) andW polarimetry in stage-two spin-correlation functions (S2SC). In this paper, the most general Lorentz-invariant decay density matrix is obtained for the decay sequence \(t\rightarrow W^{+}b\) where \(b\rightarrow l^{-}\bar{\nu}c\) and \(W^{+}\rightarrow l^{+}\nu _{l}\) [or \(W^{+}\rightarrow j\overline{_{d}}j_u\)], and likewise for \(\bar{t} \rightarrow W^- \bar{b}\). These density matrices are expressed in terms of b-polarimetry helicity parameters which enable a unique determination of the relative phases among the \(A(\lambda_{W^+},\lambda_b)\) amplitudes. Thereby, S2SC distributions and single-sided b-W-interference distributions are expressed in terms of these parameters. The four b-polarimetry helicity parameters involving the \(A(-1,-1/2)\) amplitude are considered in detail. \(\Lambda_b\) polarimetry signatures will not be suppressed in top quark analyses when final \(\bar{\nu}\) angles-and-energy variables are used for \(b\rightarrow l^{-}\bar{\nu}c\).

Keywords

Density Matrix Relative Phasis Density Matrice Large Mass Helicity Amplitude 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • C.A. Nelson
    • 1
  1. 1.Department of Physics, State University of New York at Binghamton, Binghamton, NY 13902-6016, USA US

Personalised recommendations