Measurement of the top-quark mass in the all-hadronic channel using the full CDF data set

The top-quark mass $M_{\text{top}}$ is measured using top quark-antiquark pairs produced in proton-antiproton collisions at a center-of-mass energy of 1.96 TeV and that decay into a fully hadronic final state. The full data set collected with the CDF II detector at the Fermilab Tevatron Collider, corresponding to an integrated luminosity of $9.3{\mathrm{fb}}^{-1}$, is used. Events are selected that have six to eight jets, at least one of which is identified as having originated from a $b$ quark. In addition, a multivariate algorithm, containing multiple kinematic variables as inputs, is used to discriminate signal events from background events due to QCD multijet production. Templates for the reconstructed top-quark mass are combined in a likelihood fit to measure $M_{\text{top}}$ with a simultaneous calibration of the jet energy scale. A value of $M_{\text{top}}=175.07\pm 1.19(\mathrm{stat}){}_{-1.58}^{+1.55}(\mathrm{syst})\mathrm{GeV}/c^2$ is obtained for the top-quark mass.

Figure 1

Templates of $m_t^{\text{rec}}$ for events with two or three tags and corresponding probability density functions superimposed. (a) The signal PDF $P_{\mathrm{s}}$, for various values of $M_{\text{top}}$ and ${\mathrm{\Delta }}_{\text{JES}}=0$. (b) The background PDF $P_{\mathrm{b}}$.

Figure 2

Negative log-likelihood contours for the likelihood fit performed for the $M_{\text{top}}$ and ${\mathrm{\Delta }}_{\text{JES}}$ measurement, before calibration, for events with one, two, or three tags. The minimum is shown along with the contours whose projections correspond to one, two, and three $\sigma$ uncertainties on the $M_{\text{top}}$ and ${\mathrm{\Delta }}_{\text{JES}}$ measurements.

Figure 3

Distributions of (a) $m_t^{\text{rec}}$ and (b) $m_W^{\text{rec}}$ for events with one, two, or three tags (black dots), compared to the distributions from background and signal corresponding to the measured values of $M_{\text{top}}$ and ${\mathrm{\Delta }}_{\text{JES}}$. The expected distributions are normalized to the yields returned by the best fit.