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}

We first use the $P_T(\ell \ell)$ method to predict the number of events 
in control region A, defined in Sec.~\ref{sec:abcd} as 
$125<{\rm SumJetPt}>300$~GeV and $\met/\sqrt{\rm SumJetPt}>$8.5.
We count the number of events in region
$A'$, defined in Sec.~\ref{sec:othBG} by replacing the above $\met/\sqrt{\rm SumJetPt}$
cut with the same cut on the quantity $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$,
and find $N_{A'}=6$. To predict the yield in region A we take 
$N_A = K \cdot K_C \cdot N_{A'} = 10.4 \pm 4.2$
(statistical uncertainty only, assuming Gaussian errors),
where we have taken $K = 1.73$ and $K_C = 1$. This yield is in good
agreement with the observed yield of 11 events, as shown in 
Table~\ref{tab:victory_control} and displayed in Fig.~\ref{fig:victory} (left).
{\color{red} \bf Perform DY estimate for this control region}.

Encouraged by the good agreement between predicted and observed yields
in the control region, we proceed to perform the $P_T(\ell \ell)$ method 
in the signal region ${\rm SumJetPt}>300$~GeV.
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 
$N_D = K \cdot K_C \cdot N_{D'} = 3.07 \pm 2.17$ where $K=1.54 \pm xx$ 
as derived in Sec.~\ref{sec:victory} and $K_C = 1$.
We next subtract off the expected DY contribution of 
{\color{red} \bf 0.8 $\pm$ 0.8 (update DY estimate)} events, as calculated 
in Sec.~\ref{sec:othBG}. This gives a predicted yield of
$N_D=1.8^{+2.5}_{-1.8}$ events, which is consistent with the observed yield of
1 event, as summarized in Table~\ref{tab:victory_signal} and Fig.~\ref{fig:victory}
(right).


\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_signal}
\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 &  $1.8^{+2.5}_{-1.8}$     &          1 &      0.56 \\
\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:	1.12
Committed:	Thu Nov 11 15:43:50 2010 UTC (14 years, 5 months ago) by benhoob
Content type:	application/x-tex
Branch:	MAIN
Changes since 1.11:	+12 -10 lines
Log Message:	Minor updates
#	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	benhoob	1.11	We first use the $P_T(\ell \ell)$ method to predict the number of events
69	benhoob	1.12	in control region A, defined in Sec.~\ref{sec:abcd} as
70			$125<{\rm SumJetPt}>300$~GeV and $\met/\sqrt{\rm SumJetPt}>$8.5.
71			We count the number of events in region
72			$A'$, defined in Sec.~\ref{sec:othBG} by replacing the above $\met/\sqrt{\rm SumJetPt}$
73			cut with the same cut on the quantity $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$,
74			and find $N_{A'}=6$. To predict the yield in region A we take
75			$N_A = K \cdot K_C \cdot N_{A'} = 10.4 \pm 4.2$
76	benhoob	1.11	(statistical uncertainty only, assuming Gaussian errors),
77			where we have taken $K = 1.73$ and $K_C = 1$. This yield is in good
78			agreement with the observed yield of 11 events, as shown in
79			Table~\ref{tab:victory_control} and displayed in Fig.~\ref{fig:victory} (left).
80	benhoob	1.12	{\color{red} \bf Perform DY estimate for this control region}.
81	benhoob	1.11
82			Encouraged by the good agreement between predicted and observed yields
83			in the control region, we proceed to perform the $P_T(\ell \ell)$ method
84			in the signal region ${\rm SumJetPt}>300$~GeV.
85	claudioc	1.2	The number of data events in region $D'$, which is defined in
86			Section~\ref{sec:othBG} to be the same as region $D$ but with the
87			$\met/\sqrt{\rm SumJetPt}$ requirement
88	benhoob	1.11	replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement,
89	benhoob	1.8	is $N_{D'}=2$. Thus the BG prediction is
90	benhoob	1.11	$N_D = K \cdot K_C \cdot N_{D'} = 3.07 \pm 2.17$ where $K=1.54 \pm xx$
91			as derived in Sec.~\ref{sec:victory} and $K_C = 1$.
92			We next subtract off the expected DY contribution of
93			{\color{red} \bf 0.8 $\pm$ 0.8 (update DY estimate)} events, as calculated
94			in Sec.~\ref{sec:othBG}. This gives a predicted yield of
95			$N_D=1.8^{+2.5}_{-1.8}$ events, which is consistent with the observed yield of
96	benhoob	1.12	1 event, as summarized in Table~\ref{tab:victory_signal} and Fig.~\ref{fig:victory}
97			(right).
98	benhoob	1.11
99	claudioc	1.1
100	claudioc	1.3	\begin{figure}[hbt]
101			\begin{center}
102	benhoob	1.8	\includegraphics[width=0.48\linewidth]{victory_control_35pb.png}
103			\includegraphics[width=0.48\linewidth]{victory_signal_35pb.png}
104	claudioc	1.3	\caption{\label{fig:victory}\protect Distributions of
105			tcMet/$\sqrt{\rm SumJetPt}$ for the control and signal region.
106			We show the oberved distributions in both Monte Carlo and data.
107			We also show the distributions predicted from
108	benhoob	1.4	${P_T(\ell\ell)}/\sqrt{\rm SumJetPt}$ in both MC and data.}
109	claudioc	1.3	\end{center}
110			\end{figure}
111
112
113	benhoob	1.9	\begin{table}[hbt]
114			\begin{center}
115			\label{tab:victory_control}
116			\caption{Results of the dilepton $p_{T}$ template method in the control region
117			$125 < \mathrm{sumJetPt} < 300$~GeV. The predicted and observed yields for
118			the region $\mathrm{tcmet}/\sqrt{\mathrm{sumJetPt}}>$~8.5 are shown for data
119			and MC. The error on the prediction for data is statistical only, assuming
120			Gaussian errors.}
121			\begin{tabular}{l\|c\|c\|c}
122			\hline
123			& Predicted & Observed & Obs/Pred \\
124			\hline
125			total SM MC & 7.10 & 8.61 & 1.21 \\
126			data & 10.38 $\pm$ 4.24 & 11 & 1.06 \\
127			\hline
128			\end{tabular}
129			\end{center}
130			\end{table}
131
132			\begin{table}[hbt]
133			\begin{center}
134	benhoob	1.12	\label{tab:victory_signal}
135	benhoob	1.9	\caption{Results of the dilepton $p_{T}$ template method in the signal region
136			$125 < \mathrm{sumJetPt} < 300$~GeV. The predicted and observed yields for
137			the region $\mathrm{tcmet}/\sqrt{\mathrm{sumJetPt}}>$~8.5 are shown for data
138			and MC. The error on the prediction for data is statistical only, assuming
139			Gaussian errors.}
140			\begin{tabular}{l\|c\|c\|c}
141			\hline
142	benhoob	1.11	& Predicted & Observed & Obs/Pred \\
143	benhoob	1.9	\hline
144	benhoob	1.11	total SM MC & 0.96 & 1.41 & 1.46 \\
145	benhoob	1.12	data & $1.8^{+2.5}_{-1.8}$ & 1 & 0.56 \\
146	benhoob	1.9	\hline
147			\end{tabular}
148			\end{center}
149			\end{table}
150
151
152	claudioc	1.3	\subsection{Summary of results}
153	claudioc	1.1	To summarize: we see no evidence for an anomalous
154			rate of opposite sign isolated dilepton events
155			at high \met and high SumJetPt. The extraction of
156			quantitative limits on new physics models is discussed
157	benhoob	1.5	in Section~\ref{sec:limit}.