claudioc/OSNote2010/results.tex

\clearpage

\section{Results}
\label{sec:results}

\begin{figure}[tbh]
\begin{center}
\includegraphics[width=0.75\linewidth]{abcd_35pb.png}
\caption{\label{fig:abcdData}\protect Distributions of SumJetPt 
vs. MET$/\sqrt{\rm SumJetPt}$ for SM Monte Carlo and data.  Here we also
show our choice of ABCD regions.}
\end{center}
\end{figure}

The data, together with SM expectations is presented 
in Figure~\ref{fig:abcdData}.  We see 1 event in the 
signal region (region $D$).  The Standard Model MC 
expectation is 1.4 events.

\subsection{Background estimate from the ABCD method}
\label{sec:abcdres}

The data yields in the 
four regions are summarized in Table~\ref{tab:datayield}.
The prediction of the ABCD method is is given by $A\times C/B$ and
is 1.5 events. (see Table~\ref{tab:datayield}.  

\begin{table}[hbt]
\begin{center}
\caption{\label{tab:datayield} Data yields in the four
regions of Figure~\ref{fig:abcdData}, as well as the predicted yield in region D given
by A$\times$C / B.  The quoted uncertainty
on the prediction in data is statistical only, assuming Gaussian errors.
We also show the SM Monte Carlo expectations, scaled to 34.85~pb$^{-1}$.}
\begin{tabular}{l||c|c|c|c||c}
\hline
         sample   &              A   &              B   &              C   &              D   & A$\times$C / B  \\
\hline
$t\bar{t}\rightarrow \ell^{+}\ell^{-}$   &           7.96   &          33.07   &           4.81   &           1.20   &           1.16  \\
$t\bar{t}\rightarrow \mathrm{other}$   &           0.15   &           0.85   &           0.09   &           0.04   &           0.02  \\
   $Z^0$ + jets   &           0.00   &           1.16   &           0.08   &           0.08   &           0.00  \\
$W^{\pm}$ + jets   &           0.00   &           0.10   &           0.00   &           0.00   &           0.00  \\
       $W^+W^-$   &           0.19   &           0.29   &           0.02   &           0.07   &           0.02  \\
   $W^{\pm}Z^0$   &           0.03   &           0.04   &           0.01   &           0.01   &           0.00  \\
       $Z^0Z^0$   &           0.00   &           0.03   &           0.00   &           0.00   &           0.00  \\
     single top   &           0.28   &           1.00   &           0.04   &           0.01   &           0.01  \\
\hline
    total SM MC   &           8.61   &          36.54   &           5.05   &           1.41   &           1.19  \\
\hline
           data   &             11   &             36   &              5   &              1   &1.53 $\pm$ 0.86  \\
\hline
\end{tabular}
\end{center}
\end{table}

%As a cross-check, we can subtract from the yields in 
%Table~\ref{tab:datayield} the expected DY contributions
%from Table~\ref{tab:ABCD-DY} in order to get a ``purer''
%estimate of the $t\bar{t}$ contribution.  The result
%of this exercise is {\color{red} xx} events.

\subsection{Background estimate from the $P_T(\ell\ell)$ method}
\label{sec:victoryres}

The number of data events in region $D'$, which is defined in 
Section~\ref{sec:othBG} to be the same as region $D$ but with the
$\met/\sqrt{\rm SumJetPt}$ requirement 
replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement
is $N_{D'}=2$.  Thus the BG prediction is 
$N_D = K \cdot K_C \cdot N_{D'} = 1.5$
where $K=1.5 \pm xx$ as derived in Sec.~\ref{sec:victory} and
$K_C = 1$.
Note that if we were to subtract off from region $D'$ 
the {\color{red} 0.8 $\pm$ 0.8} DY events estimated from
Section~\ref{sec:othBG}, the background
prediction would change to $N_D=1.8 \pm xx$ events.

%%%TO BE REPLACED
%{\color{red}As mentioned previously, for the 11/pb analysis
%we use the $K$ factor from data and take $K=1$.
%This will change for the full dataset.  We will also pay 
%more attention to the statistical errors.}

%The number of data events in region $D'$, which is defined in 
%Section~\ref{sec:othBG} to be the same as region $D$ but with the
%$\met/\sqrt{\rm SumJetPt}$ requirement 
%replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement
%is $N_{D'}=1$.  Thus the BG prediction is 
%$N_D = K \cdot K_{\rm fudge} \cdot N_{D'} = 1.5$
%where we used $K=1.5 \pm xx$ and $K_{\rm fudge}=1.0 \pm 0.0$. 
%Note that if we were to subtract off from region $D'$ 
%the {\color{red} 0.4 $\pm$ 0.4} DY events estimated from
%Section~\ref{sec:othBG}, the background
%prediction would change to $N_D=0.9 \pm xx$ events.
%{\color{red} When we do this with a real
%$K_{\rm fudge}$, the fudge factor will be different
%after the DY subtraction.}

As a cross-check, we use the $P_T(\ell \ell)$
method to also predict the number of events in the 
control region $125<{\rm SumJetPt}<300$ GeV and 
\met/$\sqrt{\rm SumJetPt} > 8.5$.  We predict 
$5.6^{+x}_{-y}$ events and we observe 4. 
The results of the $P_T(\ell\ell)$ method are
summarized in Figure~\ref{fig:victory}.

\begin{figure}[hbt]
\begin{center}
\includegraphics[width=0.48\linewidth]{victory_control_35pb.png}
\includegraphics[width=0.48\linewidth]{victory_signal_35pb.png}
\caption{\label{fig:victory}\protect Distributions of 
tcMet/$\sqrt{\rm SumJetPt}$ for the control and signal region.
We show the oberved distributions in both Monte Carlo and data.
We also show the distributions predicted from 
${P_T(\ell\ell)}/\sqrt{\rm SumJetPt}$ in both MC and data.}
\end{center}
\end{figure}


\begin{table}[hbt]
\begin{center}
\label{tab:victory_control}
\caption{Results of the dilepton $p_{T}$ template method in the control region
$125 < \mathrm{sumJetPt} < 300$~GeV. The predicted and observed yields for
the region $\mathrm{tcmet}/\sqrt{\mathrm{sumJetPt}}>$~8.5 are shown for data
and MC. The error on the prediction for data is statistical only, assuming
Gaussian errors.}
\begin{tabular}{l|c|c|c}
\hline
              & Predicted           &   Observed &  Obs/Pred \\
\hline
total SM   MC &      7.10           &       8.61 &      1.21 \\
         data &    10.38 $\pm$ 4.24 &         11 &      1.06 \\
\hline
\end{tabular}
\end{center}
\end{table}

\begin{table}[hbt]
\begin{center}
\label{tab:victory_control}
\caption{Results of the dilepton $p_{T}$ template method in the signal region
$125 < \mathrm{sumJetPt} < 300$~GeV. The predicted and observed yields for
the region $\mathrm{tcmet}/\sqrt{\mathrm{sumJetPt}}>$~8.5 are shown for data
and MC. The error on the prediction for data is statistical only, assuming
Gaussian errors.}
\begin{tabular}{l|c|c|c}
\hline
              & Predicted           &   Observed &  Obs/Pred \\
\hline
total SM   MC &      0.96           &       1.41 &      1.46 \\
         data &     3.07 $\pm$ 2.17 &          1 &      0.33 \\
\hline
\end{tabular}
\end{center}
\end{table}


\subsection{Summary of results}
To summarize: we see no evidence for an anomalous 
rate of opposite sign isolated dilepton events
at high \met and high SumJetPt.  The extraction of 
quantitative limits on new physics models is discussed
in Section~\ref{sec:limit}.
Revision:	1.9
Committed:	Thu Nov 11 09:20:56 2010 UTC (14 years, 5 months ago) by benhoob
Content type:	application/x-tex
Branch:	MAIN
Changes since 1.8:	+39 -0 lines
Log Message:	Update victory results
#	User	Rev	Content
1	benhoob	1.7	\clearpage
2
3	claudioc	1.1	\section{Results}
4			\label{sec:results}
5
6			\begin{figure}[tbh]
7			\begin{center}
8	benhoob	1.7	\includegraphics[width=0.75\linewidth]{abcd_35pb.png}
9	claudioc	1.1	\caption{\label{fig:abcdData}\protect Distributions of SumJetPt
10			vs. MET$/\sqrt{\rm SumJetPt}$ for SM Monte Carlo and data. Here we also
11			show our choice of ABCD regions.}
12			\end{center}
13			\end{figure}
14
15	claudioc	1.2	The data, together with SM expectations is presented
16	benhoob	1.7	in Figure~\ref{fig:abcdData}. We see 1 event in the
17			signal region (region $D$). The Standard Model MC
18			expectation is 1.4 events.
19	claudioc	1.2
20			\subsection{Background estimate from the ABCD method}
21			\label{sec:abcdres}
22
23			The data yields in the
24			four regions are summarized in Table~\ref{tab:datayield}.
25	benhoob	1.7	The prediction of the ABCD method is is given by $A\times C/B$ and
26			is 1.5 events. (see Table~\ref{tab:datayield}.
27	claudioc	1.2
28	claudioc	1.1	\begin{table}[hbt]
29			\begin{center}
30			\caption{\label{tab:datayield} Data yields in the four
31	benhoob	1.7	regions of Figure~\ref{fig:abcdData}, as well as the predicted yield in region D given
32			by A$\times$C / B. The quoted uncertainty
33	benhoob	1.4	on the prediction in data is statistical only, assuming Gaussian errors.
34	benhoob	1.7	We also show the SM Monte Carlo expectations, scaled to 34.85~pb$^{-1}$.}
35			\begin{tabular}{l\|\|c\|c\|c\|c\|\|c}
36			\hline
37			sample & A & B & C & D & A$\times$C / B \\
38			\hline
39			$t\bar{t}\rightarrow \ell^{+}\ell^{-}$ & 7.96 & 33.07 & 4.81 & 1.20 & 1.16 \\
40			$t\bar{t}\rightarrow \mathrm{other}$ & 0.15 & 0.85 & 0.09 & 0.04 & 0.02 \\
41			$Z^0$ + jets & 0.00 & 1.16 & 0.08 & 0.08 & 0.00 \\
42			$W^{\pm}$ + jets & 0.00 & 0.10 & 0.00 & 0.00 & 0.00 \\
43			$W^+W^-$ & 0.19 & 0.29 & 0.02 & 0.07 & 0.02 \\
44			$W^{\pm}Z^0$ & 0.03 & 0.04 & 0.01 & 0.01 & 0.00 \\
45			$Z^0Z^0$ & 0.00 & 0.03 & 0.00 & 0.00 & 0.00 \\
46			single top & 0.28 & 1.00 & 0.04 & 0.01 & 0.01 \\
47			\hline
48			total SM MC & 8.61 & 36.54 & 5.05 & 1.41 & 1.19 \\
49	claudioc	1.1	\hline
50	benhoob	1.7	data & 11 & 36 & 5 & 1 &1.53 $\pm$ 0.86 \\
51	claudioc	1.1	\hline
52			\end{tabular}
53			\end{center}
54			\end{table}
55
56	claudioc	1.3	%As a cross-check, we can subtract from the yields in
57			%Table~\ref{tab:datayield} the expected DY contributions
58			%from Table~\ref{tab:ABCD-DY} in order to get a ``purer''
59			%estimate of the $t\bar{t}$ contribution. The result
60			%of this exercise is {\color{red} xx} events.
61	claudioc	1.2
62			\subsection{Background estimate from the $P_T(\ell\ell)$ method}
63			\label{sec:victoryres}
64
65			The number of data events in region $D'$, which is defined in
66			Section~\ref{sec:othBG} to be the same as region $D$ but with the
67			$\met/\sqrt{\rm SumJetPt}$ requirement
68			replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement
69	benhoob	1.8	is $N_{D'}=2$. Thus the BG prediction is
70	benhoob	1.6	$N_D = K \cdot K_C \cdot N_{D'} = 1.5$
71			where $K=1.5 \pm xx$ as derived in Sec.~\ref{sec:victory} and
72			$K_C = 1$.
73	claudioc	1.2	Note that if we were to subtract off from region $D'$
74	benhoob	1.8	the {\color{red} 0.8 $\pm$ 0.8} DY events estimated from
75	claudioc	1.3	Section~\ref{sec:othBG}, the background
76	benhoob	1.8	prediction would change to $N_D=1.8 \pm xx$ events.
77	benhoob	1.4
78			%%%TO BE REPLACED
79			%{\color{red}As mentioned previously, for the 11/pb analysis
80			%we use the $K$ factor from data and take $K=1$.
81			%This will change for the full dataset. We will also pay
82			%more attention to the statistical errors.}
83
84			%The number of data events in region $D'$, which is defined in
85			%Section~\ref{sec:othBG} to be the same as region $D$ but with the
86			%$\met/\sqrt{\rm SumJetPt}$ requirement
87			%replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement
88			%is $N_{D'}=1$. Thus the BG prediction is
89			%$N_D = K \cdot K_{\rm fudge} \cdot N_{D'} = 1.5$
90			%where we used $K=1.5 \pm xx$ and $K_{\rm fudge}=1.0 \pm 0.0$.
91			%Note that if we were to subtract off from region $D'$
92			%the {\color{red} 0.4 $\pm$ 0.4} DY events estimated from
93			%Section~\ref{sec:othBG}, the background
94			%prediction would change to $N_D=0.9 \pm xx$ events.
95			%{\color{red} When we do this with a real
96			%$K_{\rm fudge}$, the fudge factor will be different
97			%after the DY subtraction.}
98	claudioc	1.1
99	claudioc	1.2	As a cross-check, we use the $P_T(\ell \ell)$
100	claudioc	1.1	method to also predict the number of events in the
101	benhoob	1.8	control region $125<{\rm SumJetPt}<300$ GeV and
102	claudioc	1.1	\met/$\sqrt{\rm SumJetPt} > 8.5$. We predict
103			$5.6^{+x}_{-y}$ events and we observe 4.
104	claudioc	1.3	The results of the $P_T(\ell\ell)$ method are
105			summarized in Figure~\ref{fig:victory}.
106	claudioc	1.1
107	claudioc	1.3	\begin{figure}[hbt]
108			\begin{center}
109	benhoob	1.8	\includegraphics[width=0.48\linewidth]{victory_control_35pb.png}
110			\includegraphics[width=0.48\linewidth]{victory_signal_35pb.png}
111	claudioc	1.3	\caption{\label{fig:victory}\protect Distributions of
112			tcMet/$\sqrt{\rm SumJetPt}$ for the control and signal region.
113			We show the oberved distributions in both Monte Carlo and data.
114			We also show the distributions predicted from
115	benhoob	1.4	${P_T(\ell\ell)}/\sqrt{\rm SumJetPt}$ in both MC and data.}
116	claudioc	1.3	\end{center}
117			\end{figure}
118
119
120	benhoob	1.9	\begin{table}[hbt]
121			\begin{center}
122			\label{tab:victory_control}
123			\caption{Results of the dilepton $p_{T}$ template method in the control region
124			$125 < \mathrm{sumJetPt} < 300$~GeV. The predicted and observed yields for
125			the region $\mathrm{tcmet}/\sqrt{\mathrm{sumJetPt}}>$~8.5 are shown for data
126			and MC. The error on the prediction for data is statistical only, assuming
127			Gaussian errors.}
128			\begin{tabular}{l\|c\|c\|c}
129			\hline
130			& Predicted & Observed & Obs/Pred \\
131			\hline
132			total SM MC & 7.10 & 8.61 & 1.21 \\
133			data & 10.38 $\pm$ 4.24 & 11 & 1.06 \\
134			\hline
135			\end{tabular}
136			\end{center}
137			\end{table}
138
139			\begin{table}[hbt]
140			\begin{center}
141			\label{tab:victory_control}
142			\caption{Results of the dilepton $p_{T}$ template method in the signal region
143			$125 < \mathrm{sumJetPt} < 300$~GeV. The predicted and observed yields for
144			the region $\mathrm{tcmet}/\sqrt{\mathrm{sumJetPt}}>$~8.5 are shown for data
145			and MC. The error on the prediction for data is statistical only, assuming
146			Gaussian errors.}
147			\begin{tabular}{l\|c\|c\|c}
148			\hline
149			& Predicted & Observed & Obs/Pred \\
150			\hline
151			total SM MC & 0.96 & 1.41 & 1.46 \\
152			data & 3.07 $\pm$ 2.17 & 1 & 0.33 \\
153			\hline
154			\end{tabular}
155			\end{center}
156			\end{table}
157
158
159	claudioc	1.3	\subsection{Summary of results}
160	claudioc	1.1	To summarize: we see no evidence for an anomalous
161			rate of opposite sign isolated dilepton events
162			at high \met and high SumJetPt. The extraction of
163			quantitative limits on new physics models is discussed
164	benhoob	1.5	in Section~\ref{sec:limit}.