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 $\pm$ 0.9 events (statistical uncertainty only, assuming
Gaussian errors). (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.

\clearpage

\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:	1.10
Committed:	Thu Nov 11 12:44:58 2010 UTC (14 years, 5 months ago) by benhoob
Content type:	application/x-tex
Branch:	MAIN
Changes since 1.9:	+4 -1 lines
Log Message:	Added stat error to ABCD prediction
#	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	benhoob	1.10	is 1.5 $\pm$ 0.9 events (statistical uncertainty only, assuming
27			Gaussian errors). (see Table~\ref{tab:datayield}).
28	claudioc	1.2
29	claudioc	1.1	\begin{table}[hbt]
30			\begin{center}
31			\caption{\label{tab:datayield} Data yields in the four
32	benhoob	1.7	regions of Figure~\ref{fig:abcdData}, as well as the predicted yield in region D given
33			by A$\times$C / B. The quoted uncertainty
34	benhoob	1.4	on the prediction in data is statistical only, assuming Gaussian errors.
35	benhoob	1.7	We also show the SM Monte Carlo expectations, scaled to 34.85~pb$^{-1}$.}
36			\begin{tabular}{l\|\|c\|c\|c\|c\|\|c}
37			\hline
38			sample & A & B & C & D & A$\times$C / B \\
39			\hline
40			$t\bar{t}\rightarrow \ell^{+}\ell^{-}$ & 7.96 & 33.07 & 4.81 & 1.20 & 1.16 \\
41			$t\bar{t}\rightarrow \mathrm{other}$ & 0.15 & 0.85 & 0.09 & 0.04 & 0.02 \\
42			$Z^0$ + jets & 0.00 & 1.16 & 0.08 & 0.08 & 0.00 \\
43			$W^{\pm}$ + jets & 0.00 & 0.10 & 0.00 & 0.00 & 0.00 \\
44			$W^+W^-$ & 0.19 & 0.29 & 0.02 & 0.07 & 0.02 \\
45			$W^{\pm}Z^0$ & 0.03 & 0.04 & 0.01 & 0.01 & 0.00 \\
46			$Z^0Z^0$ & 0.00 & 0.03 & 0.00 & 0.00 & 0.00 \\
47			single top & 0.28 & 1.00 & 0.04 & 0.01 & 0.01 \\
48			\hline
49			total SM MC & 8.61 & 36.54 & 5.05 & 1.41 & 1.19 \\
50	claudioc	1.1	\hline
51	benhoob	1.7	data & 11 & 36 & 5 & 1 &1.53 $\pm$ 0.86 \\
52	claudioc	1.1	\hline
53			\end{tabular}
54			\end{center}
55			\end{table}
56
57	claudioc	1.3	%As a cross-check, we can subtract from the yields in
58			%Table~\ref{tab:datayield} the expected DY contributions
59			%from Table~\ref{tab:ABCD-DY} in order to get a ``purer''
60			%estimate of the $t\bar{t}$ contribution. The result
61			%of this exercise is {\color{red} xx} events.
62	claudioc	1.2
63	benhoob	1.10	\clearpage
64
65	claudioc	1.2	\subsection{Background estimate from the $P_T(\ell\ell)$ method}
66			\label{sec:victoryres}
67
68			The number of data events in region $D'$, which is defined in
69			Section~\ref{sec:othBG} to be the same as region $D$ but with the
70			$\met/\sqrt{\rm SumJetPt}$ requirement
71			replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement
72	benhoob	1.8	is $N_{D'}=2$. Thus the BG prediction is
73	benhoob	1.6	$N_D = K \cdot K_C \cdot N_{D'} = 1.5$
74			where $K=1.5 \pm xx$ as derived in Sec.~\ref{sec:victory} and
75			$K_C = 1$.
76	claudioc	1.2	Note that if we were to subtract off from region $D'$
77	benhoob	1.8	the {\color{red} 0.8 $\pm$ 0.8} DY events estimated from
78	claudioc	1.3	Section~\ref{sec:othBG}, the background
79	benhoob	1.8	prediction would change to $N_D=1.8 \pm xx$ events.
80	benhoob	1.4
81			%%%TO BE REPLACED
82			%{\color{red}As mentioned previously, for the 11/pb analysis
83			%we use the $K$ factor from data and take $K=1$.
84			%This will change for the full dataset. We will also pay
85			%more attention to the statistical errors.}
86
87			%The number of data events in region $D'$, which is defined in
88			%Section~\ref{sec:othBG} to be the same as region $D$ but with the
89			%$\met/\sqrt{\rm SumJetPt}$ requirement
90			%replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement
91			%is $N_{D'}=1$. Thus the BG prediction is
92			%$N_D = K \cdot K_{\rm fudge} \cdot N_{D'} = 1.5$
93			%where we used $K=1.5 \pm xx$ and $K_{\rm fudge}=1.0 \pm 0.0$.
94			%Note that if we were to subtract off from region $D'$
95			%the {\color{red} 0.4 $\pm$ 0.4} DY events estimated from
96			%Section~\ref{sec:othBG}, the background
97			%prediction would change to $N_D=0.9 \pm xx$ events.
98			%{\color{red} When we do this with a real
99			%$K_{\rm fudge}$, the fudge factor will be different
100			%after the DY subtraction.}
101	claudioc	1.1
102	claudioc	1.2	As a cross-check, we use the $P_T(\ell \ell)$
103	claudioc	1.1	method to also predict the number of events in the
104	benhoob	1.8	control region $125<{\rm SumJetPt}<300$ GeV and
105	claudioc	1.1	\met/$\sqrt{\rm SumJetPt} > 8.5$. We predict
106			$5.6^{+x}_{-y}$ events and we observe 4.
107	claudioc	1.3	The results of the $P_T(\ell\ell)$ method are
108			summarized in Figure~\ref{fig:victory}.
109	claudioc	1.1
110	claudioc	1.3	\begin{figure}[hbt]
111			\begin{center}
112	benhoob	1.8	\includegraphics[width=0.48\linewidth]{victory_control_35pb.png}
113			\includegraphics[width=0.48\linewidth]{victory_signal_35pb.png}
114	claudioc	1.3	\caption{\label{fig:victory}\protect Distributions of
115			tcMet/$\sqrt{\rm SumJetPt}$ for the control and signal region.
116			We show the oberved distributions in both Monte Carlo and data.
117			We also show the distributions predicted from
118	benhoob	1.4	${P_T(\ell\ell)}/\sqrt{\rm SumJetPt}$ in both MC and data.}
119	claudioc	1.3	\end{center}
120			\end{figure}
121
122
123	benhoob	1.9	\begin{table}[hbt]
124			\begin{center}
125			\label{tab:victory_control}
126			\caption{Results of the dilepton $p_{T}$ template method in the control region
127			$125 < \mathrm{sumJetPt} < 300$~GeV. The predicted and observed yields for
128			the region $\mathrm{tcmet}/\sqrt{\mathrm{sumJetPt}}>$~8.5 are shown for data
129			and MC. The error on the prediction for data is statistical only, assuming
130			Gaussian errors.}
131			\begin{tabular}{l\|c\|c\|c}
132			\hline
133			& Predicted & Observed & Obs/Pred \\
134			\hline
135			total SM MC & 7.10 & 8.61 & 1.21 \\
136			data & 10.38 $\pm$ 4.24 & 11 & 1.06 \\
137			\hline
138			\end{tabular}
139			\end{center}
140			\end{table}
141
142			\begin{table}[hbt]
143			\begin{center}
144			\label{tab:victory_control}
145			\caption{Results of the dilepton $p_{T}$ template method in the signal region
146			$125 < \mathrm{sumJetPt} < 300$~GeV. The predicted and observed yields for
147			the region $\mathrm{tcmet}/\sqrt{\mathrm{sumJetPt}}>$~8.5 are shown for data
148			and MC. The error on the prediction for data is statistical only, assuming
149			Gaussian errors.}
150			\begin{tabular}{l\|c\|c\|c}
151			\hline
152			& Predicted & Observed & Obs/Pred \\
153			\hline
154			total SM MC & 0.96 & 1.41 & 1.46 \\
155			data & 3.07 $\pm$ 2.17 & 1 & 0.33 \\
156			\hline
157			\end{tabular}
158			\end{center}
159			\end{table}
160
161
162	claudioc	1.3	\subsection{Summary of results}
163	claudioc	1.1	To summarize: we see no evidence for an anomalous
164			rate of opposite sign isolated dilepton events
165			at high \met and high SumJetPt. The extraction of
166			quantitative limits on new physics models is discussed
167	benhoob	1.5	in Section~\ref{sec:limit}.