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 $D$).  For more information about
this one candidate events, see Appendix~\ref{sec:cand}.
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), as shown in 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  \\
$Z^0 \rightarrow \ell^{+}\ell^{-}$       &           0.03   &           1.47   &           0.10   &           0.10   &           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                          &           8.63   &          36.85   &           5.07   &           1.43   &           1.19  \\
\hline
           data                          &             11   &             36   &              5   &              1   &  $1.53\pm0.86$  \\
\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 $N_{A'}=6$. We subtract off the expected DY contribution in this region
$N_{DY} = 2.5 \pm 2.4$, derived in Sec.~\ref{sec:othBG}.
To predict the yield in region A we take 
$N_A = K \cdot K_C \cdot ( N_{A'} - N_{DY} ) = 6.1 \pm 6.0$
(statistical uncertainty only, assuming Gaussian errors),
where we have taken $K = 1.73$ and $K_C = 1$. This yield is consistent
with the observed yield of 11 events, as shown in 
Table~\ref{tab:victory_control} and displayed in Fig.~\ref{fig:victory} (left).

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 $N_{D'}=2$.  
We next subtract off the expected DY contribution of 
$N_{DY}$ = $0.4 \pm 0.4$ events, as calculated 
in Sec.~\ref{sec:othBG}. The BG prediction is 
$N_D = K \cdot K_C \cdot (N_{D'}-N_{DY}) = 2.5 \pm 2.2$ (statistical
uncertainty only, assuming Gaussian errors), where $K=1.54 \pm xx$ 
as derived in Sec.~\ref{sec:victory} and $K_C = 1$.
This prediction 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}
\caption{\label{tab:victory_control}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}{lccc}
\hline
              & Predicted           &   Observed &  Obs/Pred \\
\hline
total SM   MC &      7.18           &       8.63 &      1.20 \\
         data &    $6.06 \pm 5.95$  &         11 &      1.82 \\
\hline
\end{tabular}
\end{center}
\end{table}

\begin{table}[hbt]
\begin{center}
\caption{\label{tab:victory_signal}Results of the dilepton $p_{T}$ template method in the signal region
$\mathrm{sumJetPt} > 300$~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}{lccc}
\hline
              & Predicted                &   Observed &  Obs/Pred \\
\hline
total SM   MC &      1.03                &       1.43 &      1.38 \\
         data &    $2.53 \pm 2.25$       &          1 &      0.40 \\
\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:	1.17
Committed:	Sun Nov 14 13:47:56 2010 UTC (14 years, 5 months ago) by claudioc
Content type:	application/x-tex
Branch:	MAIN
Changes since 1.16:	+3 -2 lines
Log Message:	Info on candidate event
#	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	claudioc	1.17	signal region (region $D$). For more information about
18			this one candidate events, see Appendix~\ref{sec:cand}.
19			The Standard Model MC expectation is 1.4 events.
20	claudioc	1.2
21			\subsection{Background estimate from the ABCD method}
22			\label{sec:abcdres}
23
24			The data yields in the
25			four regions are summarized in Table~\ref{tab:datayield}.
26	benhoob	1.7	The prediction of the ABCD method is is given by $A\times C/B$ and
27	benhoob	1.10	is 1.5 $\pm$ 0.9 events (statistical uncertainty only, assuming
28	benhoob	1.13	Gaussian errors), as shown in Table~\ref{tab:datayield}.
29	claudioc	1.2
30	claudioc	1.1	\begin{table}[hbt]
31			\begin{center}
32			\caption{\label{tab:datayield} Data yields in the four
33	benhoob	1.7	regions of Figure~\ref{fig:abcdData}, as well as the predicted yield in region D given
34	benhoob	1.14	by A $\times$C / B. The quoted uncertainty
35	benhoob	1.4	on the prediction in data is statistical only, assuming Gaussian errors.
36	benhoob	1.7	We also show the SM Monte Carlo expectations, scaled to 34.85~pb$^{-1}$.}
37			\begin{tabular}{l\|\|c\|c\|c\|c\|\|c}
38			\hline
39	benhoob	1.14	sample & A & B & C & D & A $\times$ C / B \\
40	benhoob	1.7	\hline
41	benhoob	1.14
42	benhoob	1.7	$t\bar{t}\rightarrow \ell^{+}\ell^{-}$ & 7.96 & 33.07 & 4.81 & 1.20 & 1.16 \\
43	benhoob	1.14	$t\bar{t}\rightarrow \mathrm{other}$ & 0.15 & 0.85 & 0.09 & 0.04 & 0.02 \\
44			$Z^0 \rightarrow \ell^{+}\ell^{-}$ & 0.03 & 1.47 & 0.10 & 0.10 & 0.00 \\
45			$W^{\pm}$ + jets & 0.00 & 0.10 & 0.00 & 0.00 & 0.00 \\
46			$W^+W^-$ & 0.19 & 0.29 & 0.02 & 0.07 & 0.02 \\
47			$W^{\pm}Z^0$ & 0.03 & 0.04 & 0.01 & 0.01 & 0.00 \\
48			$Z^0Z^0$ & 0.00 & 0.03 & 0.00 & 0.00 & 0.00 \\
49			single top & 0.28 & 1.00 & 0.04 & 0.01 & 0.01 \\
50	benhoob	1.7	\hline
51	benhoob	1.14	total SM MC & 8.63 & 36.85 & 5.07 & 1.43 & 1.19 \\
52	claudioc	1.1	\hline
53	benhoob	1.14	data & 11 & 36 & 5 & 1 & $1.53\pm0.86$ \\
54	claudioc	1.1	\hline
55			\end{tabular}
56			\end{center}
57			\end{table}
58
59	claudioc	1.3	%As a cross-check, we can subtract from the yields in
60			%Table~\ref{tab:datayield} the expected DY contributions
61			%from Table~\ref{tab:ABCD-DY} in order to get a ``purer''
62			%estimate of the $t\bar{t}$ contribution. The result
63			%of this exercise is {\color{red} xx} events.
64	claudioc	1.2
65	benhoob	1.10	\clearpage
66
67	claudioc	1.2	\subsection{Background estimate from the $P_T(\ell\ell)$ method}
68			\label{sec:victoryres}
69
70	benhoob	1.11	We first use the $P_T(\ell \ell)$ method to predict the number of events
71	benhoob	1.12	in control region A, defined in Sec.~\ref{sec:abcd} as
72			$125<{\rm SumJetPt}>300$~GeV and $\met/\sqrt{\rm SumJetPt}>$8.5.
73			We count the number of events in region
74			$A'$, defined in Sec.~\ref{sec:othBG} by replacing the above $\met/\sqrt{\rm SumJetPt}$
75			cut with the same cut on the quantity $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$,
76	benhoob	1.15	and find $N_{A'}=6$. We subtract off the expected DY contribution in this region
77			$N_{DY} = 2.5 \pm 2.4$, derived in Sec.~\ref{sec:othBG}.
78			To predict the yield in region A we take
79			$N_A = K \cdot K_C \cdot ( N_{A'} - N_{DY} ) = 6.1 \pm 6.0$
80	benhoob	1.11	(statistical uncertainty only, assuming Gaussian errors),
81	benhoob	1.15	where we have taken $K = 1.73$ and $K_C = 1$. This yield is consistent
82			with the observed yield of 11 events, as shown in
83	benhoob	1.11	Table~\ref{tab:victory_control} and displayed in Fig.~\ref{fig:victory} (left).
84
85			Encouraged by the good agreement between predicted and observed yields
86			in the control region, we proceed to perform the $P_T(\ell \ell)$ method
87			in the signal region ${\rm SumJetPt}>300$~GeV.
88	claudioc	1.2	The number of data events in region $D'$, which is defined in
89			Section~\ref{sec:othBG} to be the same as region $D$ but with the
90			$\met/\sqrt{\rm SumJetPt}$ requirement
91	benhoob	1.11	replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement,
92	benhoob	1.13	is $N_{D'}=2$.
93			We next subtract off the expected DY contribution of
94	benhoob	1.14	$N_{DY}$ = $0.4 \pm 0.4$ events, as calculated
95	benhoob	1.13	in Sec.~\ref{sec:othBG}. The BG prediction is
96	benhoob	1.14	$N_D = K \cdot K_C \cdot (N_{D'}-N_{DY}) = 2.5 \pm 2.2$ (statistical
97	benhoob	1.13	uncertainty only, assuming Gaussian errors), where $K=1.54 \pm xx$
98	benhoob	1.11	as derived in Sec.~\ref{sec:victory} and $K_C = 1$.
99	benhoob	1.13	This prediction is consistent with the observed yield of
100	benhoob	1.12	1 event, as summarized in Table~\ref{tab:victory_signal} and Fig.~\ref{fig:victory}
101			(right).
102	benhoob	1.11
103	claudioc	1.1
104	claudioc	1.3	\begin{figure}[hbt]
105			\begin{center}
106	benhoob	1.8	\includegraphics[width=0.48\linewidth]{victory_control_35pb.png}
107			\includegraphics[width=0.48\linewidth]{victory_signal_35pb.png}
108	claudioc	1.3	\caption{\label{fig:victory}\protect Distributions of
109			tcMet/$\sqrt{\rm SumJetPt}$ for the control and signal region.
110			We show the oberved distributions in both Monte Carlo and data.
111			We also show the distributions predicted from
112	benhoob	1.4	${P_T(\ell\ell)}/\sqrt{\rm SumJetPt}$ in both MC and data.}
113	claudioc	1.3	\end{center}
114			\end{figure}
115
116
117	benhoob	1.14
118	benhoob	1.9	\begin{table}[hbt]
119			\begin{center}
120	benhoob	1.13	\caption{\label{tab:victory_control}Results of the dilepton $p_{T}$ template method in the control region
121	benhoob	1.9	$125 < \mathrm{sumJetPt} < 300$~GeV. The predicted and observed yields for
122			the region $\mathrm{tcmet}/\sqrt{\mathrm{sumJetPt}}>$~8.5 are shown for data
123			and MC. The error on the prediction for data is statistical only, assuming
124			Gaussian errors.}
125	benhoob	1.13	\begin{tabular}{lccc}
126	benhoob	1.9	\hline
127			& Predicted & Observed & Obs/Pred \\
128			\hline
129	benhoob	1.14	total SM MC & 7.18 & 8.63 & 1.20 \\
130	benhoob	1.16	data & $6.06 \pm 5.95$ & 11 & 1.82 \\
131	benhoob	1.9	\hline
132			\end{tabular}
133			\end{center}
134			\end{table}
135
136			\begin{table}[hbt]
137			\begin{center}
138	benhoob	1.13	\caption{\label{tab:victory_signal}Results of the dilepton $p_{T}$ template method in the signal region
139			$\mathrm{sumJetPt} > 300$~GeV. The predicted and observed yields for
140	benhoob	1.9	the region $\mathrm{tcmet}/\sqrt{\mathrm{sumJetPt}}>$~8.5 are shown for data
141			and MC. The error on the prediction for data is statistical only, assuming
142			Gaussian errors.}
143	benhoob	1.13	\begin{tabular}{lccc}
144	benhoob	1.9	\hline
145	benhoob	1.11	& Predicted & Observed & Obs/Pred \\
146	benhoob	1.9	\hline
147	benhoob	1.14	total SM MC & 1.03 & 1.43 & 1.38 \\
148			data & $2.53 \pm 2.25$ & 1 & 0.40 \\
149	benhoob	1.9	\hline
150			\end{tabular}
151			\end{center}
152			\end{table}
153
154
155	claudioc	1.3	\subsection{Summary of results}
156	claudioc	1.1	To summarize: we see no evidence for an anomalous
157			rate of opposite sign isolated dilepton events
158			at high \met and high SumJetPt. The extraction of
159			quantitative limits on new physics models is discussed
160	benhoob	1.5	in Section~\ref{sec:limit}.