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 $1.5 \pm 0.9(stat) \pm 0.2(syst)$ events, 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   &  $1.53 \pm 0.86(stat) \pm 0.15(syst)$  \\
\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}>$8.5~GeV$^{1/2}$.
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$^{1/2}$. 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$^{1/2}$. 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:	1.19
Committed:	Mon Nov 15 10:19:58 2010 UTC (14 years, 5 months ago) by benhoob
Content type:	application/x-tex
Branch:	MAIN
Changes since 1.18:	+3 -3 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	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	1.18	is $1.5 \pm 0.9(stat) \pm 0.2(syst)$ events, as shown in 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	benhoob	1.14	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	benhoob	1.14	sample & A & B & C & D & A $\times$ C / B \\
39	benhoob	1.7	\hline
40	benhoob	1.14
41	benhoob	1.7	$t\bar{t}\rightarrow \ell^{+}\ell^{-}$ & 7.96 & 33.07 & 4.81 & 1.20 & 1.16 \\
42	benhoob	1.14	$t\bar{t}\rightarrow \mathrm{other}$ & 0.15 & 0.85 & 0.09 & 0.04 & 0.02 \\
43			$Z^0 \rightarrow \ell^{+}\ell^{-}$ & 0.03 & 1.47 & 0.10 & 0.10 & 0.00 \\
44			$W^{\pm}$ + jets & 0.00 & 0.10 & 0.00 & 0.00 & 0.00 \\
45			$W^+W^-$ & 0.19 & 0.29 & 0.02 & 0.07 & 0.02 \\
46			$W^{\pm}Z^0$ & 0.03 & 0.04 & 0.01 & 0.01 & 0.00 \\
47			$Z^0Z^0$ & 0.00 & 0.03 & 0.00 & 0.00 & 0.00 \\
48			single top & 0.28 & 1.00 & 0.04 & 0.01 & 0.01 \\
49	benhoob	1.7	\hline
50	benhoob	1.14	total SM MC & 8.63 & 36.85 & 5.07 & 1.43 & 1.19 \\
51	claudioc	1.1	\hline
52	benhoob	1.18	data & 11 & 36 & 5 & 1 & $1.53 \pm 0.86(stat) \pm 0.15(syst)$ \\
53	claudioc	1.1	\hline
54			\end{tabular}
55			\end{center}
56			\end{table}
57
58	claudioc	1.3	%As a cross-check, we can subtract from the yields in
59			%Table~\ref{tab:datayield} the expected DY contributions
60			%from Table~\ref{tab:ABCD-DY} in order to get a ``purer''
61			%estimate of the $t\bar{t}$ contribution. The result
62			%of this exercise is {\color{red} xx} events.
63	claudioc	1.2
64	benhoob	1.10	\clearpage
65
66	claudioc	1.2	\subsection{Background estimate from the $P_T(\ell\ell)$ method}
67			\label{sec:victoryres}
68
69	benhoob	1.11	We first use the $P_T(\ell \ell)$ method to predict the number of events
70	benhoob	1.12	in control region A, defined in Sec.~\ref{sec:abcd} as
71	benhoob	1.19	$125<{\rm SumJetPt}>300$~GeV and $\met/\sqrt{\rm SumJetPt}>$8.5~GeV$^{1/2}$.
72	benhoob	1.12	We count the number of events in region
73			$A'$, defined in Sec.~\ref{sec:othBG} by replacing the above $\met/\sqrt{\rm SumJetPt}$
74			cut with the same cut on the quantity $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$,
75	benhoob	1.15	and find $N_{A'}=6$. We subtract off the expected DY contribution in this region
76			$N_{DY} = 2.5 \pm 2.4$, derived in Sec.~\ref{sec:othBG}.
77			To predict the yield in region A we take
78			$N_A = K \cdot K_C \cdot ( N_{A'} - N_{DY} ) = 6.1 \pm 6.0$
79	benhoob	1.11	(statistical uncertainty only, assuming Gaussian errors),
80	benhoob	1.15	where we have taken $K = 1.73$ and $K_C = 1$. This yield is consistent
81			with the observed yield of 11 events, as shown in
82	benhoob	1.11	Table~\ref{tab:victory_control} and displayed in Fig.~\ref{fig:victory} (left).
83
84			Encouraged by the good agreement between predicted and observed yields
85			in the control region, we proceed to perform the $P_T(\ell \ell)$ method
86			in the signal region ${\rm SumJetPt}>300$~GeV.
87	claudioc	1.2	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	benhoob	1.11	replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement,
91	benhoob	1.13	is $N_{D'}=2$.
92			We next subtract off the expected DY contribution of
93	benhoob	1.14	$N_{DY}$ = $0.4 \pm 0.4$ events, as calculated
94	benhoob	1.13	in Sec.~\ref{sec:othBG}. The BG prediction is
95	benhoob	1.14	$N_D = K \cdot K_C \cdot (N_{D'}-N_{DY}) = 2.5 \pm 2.2$ (statistical
96	benhoob	1.13	uncertainty only, assuming Gaussian errors), where $K=1.54 \pm xx$
97	benhoob	1.11	as derived in Sec.~\ref{sec:victory} and $K_C = 1$.
98	benhoob	1.13	This prediction is consistent with the observed yield of
99	benhoob	1.12	1 event, as summarized in Table~\ref{tab:victory_signal} and Fig.~\ref{fig:victory}
100			(right).
101	benhoob	1.11
102	claudioc	1.1
103	claudioc	1.3	\begin{figure}[hbt]
104			\begin{center}
105	benhoob	1.8	\includegraphics[width=0.48\linewidth]{victory_control_35pb.png}
106			\includegraphics[width=0.48\linewidth]{victory_signal_35pb.png}
107	claudioc	1.3	\caption{\label{fig:victory}\protect Distributions of
108			tcMet/$\sqrt{\rm SumJetPt}$ for the control and signal region.
109			We show the oberved distributions in both Monte Carlo and data.
110			We also show the distributions predicted from
111	benhoob	1.4	${P_T(\ell\ell)}/\sqrt{\rm SumJetPt}$ in both MC and data.}
112	claudioc	1.3	\end{center}
113			\end{figure}
114
115
116	benhoob	1.14
117	benhoob	1.9	\begin{table}[hbt]
118			\begin{center}
119	benhoob	1.13	\caption{\label{tab:victory_control}Results of the dilepton $p_{T}$ template method in the control region
120	benhoob	1.19	$125 < \mathrm{sumJetPt} < 300$~GeV$^{1/2}$. The predicted and observed yields for
121	benhoob	1.9	the region $\mathrm{tcmet}/\sqrt{\mathrm{sumJetPt}}>$~8.5 are shown for data
122			and MC. The error on the prediction for data is statistical only, assuming
123			Gaussian errors.}
124	benhoob	1.13	\begin{tabular}{lccc}
125	benhoob	1.9	\hline
126			& Predicted & Observed & Obs/Pred \\
127			\hline
128	benhoob	1.14	total SM MC & 7.18 & 8.63 & 1.20 \\
129	benhoob	1.16	data & $6.06 \pm 5.95$ & 11 & 1.82 \\
130	benhoob	1.9	\hline
131			\end{tabular}
132			\end{center}
133			\end{table}
134
135			\begin{table}[hbt]
136			\begin{center}
137	benhoob	1.13	\caption{\label{tab:victory_signal}Results of the dilepton $p_{T}$ template method in the signal region
138	benhoob	1.19	$\mathrm{sumJetPt} > 300$~GeV$^{1/2}$. The predicted and observed yields for
139	benhoob	1.9	the region $\mathrm{tcmet}/\sqrt{\mathrm{sumJetPt}}>$~8.5 are shown for data
140			and MC. The error on the prediction for data is statistical only, assuming
141			Gaussian errors.}
142	benhoob	1.13	\begin{tabular}{lccc}
143	benhoob	1.9	\hline
144	benhoob	1.11	& Predicted & Observed & Obs/Pred \\
145	benhoob	1.9	\hline
146	benhoob	1.14	total SM MC & 1.03 & 1.43 & 1.38 \\
147			data & $2.53 \pm 2.25$ & 1 & 0.40 \\
148	benhoob	1.9	\hline
149			\end{tabular}
150			\end{center}
151			\end{table}
152
153
154	claudioc	1.3	\subsection{Summary of results}
155	claudioc	1.1	To summarize: we see no evidence for an anomalous
156			rate of opposite sign isolated dilepton events
157			at high \met and high SumJetPt. The extraction of
158			quantitative limits on new physics models is discussed
159	benhoob	1.5	in Section~\ref{sec:limit}.