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Revision 1.3 by claudioc, Sat Nov 6 19:51:16 2010 UTC vs.
Revision 1.5 by benhoob, Mon Nov 8 11:09:49 2010 UTC

# Line 33 | Line 33 | is 0.5 events.
33   \begin{table}[hbt]
34   \begin{center}
35   \caption{\label{tab:datayield} Data yields in the four
36 < regions of Figure~\ref{fig:abcdData}.  We also show the
37 < SM Monte Carlo expectations.}
36 > regions of Figure~\ref{fig:abcdData}.  The quoted uncertainty
37 > on the prediction in data is statistical only, assuming Gaussian errors.
38 > We also show the SM Monte Carlo expectations.}
39   \begin{tabular}{|l|c|c|c|c||c|}
40   \hline
41        &A   & B    & C   & D   & AC/B \\ \hline
42 < Data  &3   & 6    & 1   & 0   & $0.5^{+x}_{-y}$ \\
42 > Data  &3   & 6    & 1   & 0   & $0.5^{+0.6}_{-0.5}$ \\
43   SM MC &2.5 &11.2  & 1.5 & 0.4 & 0.4 \\
44   \hline
45   \end{tabular}
# Line 54 | Line 55 | SM MC &2.5 &11.2  & 1.5 & 0.4 & 0.4 \\
55   \subsection{Background estimate from the $P_T(\ell\ell)$ method}
56   \label{sec:victoryres}
57  
57
58 {\color{red}As mentioned previously, for the 11/pb analysis
59 we use the $K$ factor from data and take $K_{\rm fudge}=1$.
60 This will change for the full dataset.  We will also pay
61 more attention to the statistical errors.}
62
58   The number of data events in region $D'$, which is defined in
59   Section~\ref{sec:othBG} to be the same as region $D$ but with the
60   $\met/\sqrt{\rm SumJetPt}$ requirement
61   replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement
62   is $N_{D'}=1$.  Thus the BG prediction is
63 < $N_D = K \cdot K_{\rm fudge} \cdot N_{D'} = 1.5$
64 < where we used $K=1.5 \pm xx$ and $K_{\rm fudge}=1.0 \pm 0.0$.
63 > $N_D = K \cdot N_{D'} = 1.5$
64 > where $K=1.5 \pm xx$ as derived in Sec.~\ref{sec:victory}.
65   Note that if we were to subtract off from region $D'$
66   the {\color{red} 0.4 $\pm$ 0.4} DY events estimated from
67   Section~\ref{sec:othBG}, the background
68   prediction would change to $N_D=0.9 \pm xx$ events.
69 < {\color{red} When we do this with a real
70 < $K_{\rm fudge}$, the fudge factor will be different
71 < after the DY subtraction.}
69 >
70 > %%%TO BE REPLACED
71 > %{\color{red}As mentioned previously, for the 11/pb analysis
72 > %we use the $K$ factor from data and take $K=1$.
73 > %This will change for the full dataset.  We will also pay
74 > %more attention to the statistical errors.}
75 >
76 > %The number of data events in region $D'$, which is defined in
77 > %Section~\ref{sec:othBG} to be the same as region $D$ but with the
78 > %$\met/\sqrt{\rm SumJetPt}$ requirement
79 > %replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement
80 > %is $N_{D'}=1$.  Thus the BG prediction is
81 > %$N_D = K \cdot K_{\rm fudge} \cdot N_{D'} = 1.5$
82 > %where we used $K=1.5 \pm xx$ and $K_{\rm fudge}=1.0 \pm 0.0$.
83 > %Note that if we were to subtract off from region $D'$
84 > %the {\color{red} 0.4 $\pm$ 0.4} DY events estimated from
85 > %Section~\ref{sec:othBG}, the background
86 > %prediction would change to $N_D=0.9 \pm xx$ events.
87 > %{\color{red} When we do this with a real
88 > %$K_{\rm fudge}$, the fudge factor will be different
89 > %after the DY subtraction.}
90  
91   As a cross-check, we use the $P_T(\ell \ell)$
92   method to also predict the number of events in the
# Line 91 | Line 104 | summarized in Figure~\ref{fig:victory}.
104   tcMet/$\sqrt{\rm SumJetPt}$ for the control and signal region.
105   We show the oberved distributions in both Monte Carlo and data.
106   We also show the distributions predicted from
107 < tcMet/$\sqrt{P_T(\ell\ell)}$ in both MC and data.}
107 > ${P_T(\ell\ell)}/\sqrt{\rm SumJetPt}$ in both MC and data.}
108   \end{center}
109   \end{figure}
110  
# Line 101 | Line 114 | To summarize: we see no evidence for an
114   rate of opposite sign isolated dilepton events
115   at high \met and high SumJetPt.  The extraction of
116   quantitative limits on new physics models is discussed
117 < in Section~\ref{sec:limits}.
117 > in Section~\ref{sec:limit}.

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