claudioc/OSNote2010/results.tex

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

%\noindent {\color{red} In the 11 pb everything is very 
%simple because there are a few zeros.  This text is written
%for the full dataset under the assumption that some of these
%numbers will not be zero anymore.}

\begin{figure}[tbh]
\begin{center}
\includegraphics[width=0.75\linewidth]{abcdData.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 $\color{red} 0$ 
events in the signal region (region $D$).  The Standard Model
MC expectation is {\color{red} 0.4} 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 $AC/B$ and
is 0.5 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}.  The quoted uncertainty
on the prediction in data is statistical only, assuming Gaussian errors.
We also show the SM Monte Carlo expectations.}
\begin{tabular}{|l|c|c|c|c||c|}
\hline
      &A   & B    & C   & D   & AC/B \\ \hline
Data  &3   & 6    & 1   & 0   & $0.5^{+0.6}_{-0.5}$ \\
SM MC &2.5 &11.2  & 1.5 & 0.4 & 0.4 \\ 
\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 $N_{D'}=1$.  Thus the BG prediction is 
$N_D = K \cdot N_{D'} = 1.5$
where $K=1.5 \pm xx$ as derived in Sec.~\ref{sec:victory}.
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.

%%%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 $120<{\rm SumJetPt}<300$ 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.png}
\includegraphics[width=0.48\linewidth]{victory_sig.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}


\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:	1.5
Committed:	Mon Nov 8 11:09:49 2010 UTC (14 years, 5 months ago) by benhoob
Content type:	application/x-tex
Branch:	MAIN
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#	User	Rev	Content
1	claudioc	1.1	\section{Results}
2			\label{sec:results}
3
4	claudioc	1.3	%\noindent {\color{red} In the 11 pb everything is very
5			%simple because there are a few zeros. This text is written
6			%for the full dataset under the assumption that some of these
7			%numbers will not be zero anymore.}
8	claudioc	1.1
9			\begin{figure}[tbh]
10			\begin{center}
11			\includegraphics[width=0.75\linewidth]{abcdData.png}
12			\caption{\label{fig:abcdData}\protect Distributions of SumJetPt
13			vs. MET$/\sqrt{\rm SumJetPt}$ for SM Monte Carlo and data. Here we also
14			show our choice of ABCD regions.}
15			\end{center}
16			\end{figure}
17
18
19	claudioc	1.2	The data, together with SM expectations is presented
20			in Figure~\ref{fig:abcdData}. We see $\color{red} 0$
21			events in the signal region (region $D$). The Standard Model
22			MC expectation is {\color{red} 0.4} events.
23
24			\subsection{Background estimate from the ABCD method}
25			\label{sec:abcdres}
26
27			The data yields in the
28			four regions are summarized in Table~\ref{tab:datayield}.
29			The prediction of the ABCD method is is given by $AC/B$ and
30			is 0.5 events.
31			(see Table~\ref{tab:datayield}.
32
33	claudioc	1.1	\begin{table}[hbt]
34			\begin{center}
35			\caption{\label{tab:datayield} Data yields in the four
36	benhoob	1.4	regions of Figure~\ref{fig:abcdData}. The quoted uncertainty
37			on the prediction in data is statistical only, assuming Gaussian errors.
38			We also show the SM Monte Carlo expectations.}
39	claudioc	1.1	\begin{tabular}{\|l\|c\|c\|c\|c\|\|c\|}
40			\hline
41	claudioc	1.2	&A & B & C & D & AC/B \\ \hline
42	benhoob	1.4	Data &3 & 6 & 1 & 0 & $0.5^{+0.6}_{-0.5}$ \\
43	claudioc	1.1	SM MC &2.5 &11.2 & 1.5 & 0.4 & 0.4 \\
44			\hline
45			\end{tabular}
46			\end{center}
47			\end{table}
48
49	claudioc	1.3	%As a cross-check, we can subtract from the yields in
50			%Table~\ref{tab:datayield} the expected DY contributions
51			%from Table~\ref{tab:ABCD-DY} in order to get a ``purer''
52			%estimate of the $t\bar{t}$ contribution. The result
53			%of this exercise is {\color{red} xx} events.
54	claudioc	1.2
55			\subsection{Background estimate from the $P_T(\ell\ell)$ method}
56			\label{sec:victoryres}
57
58			The number of data events in region $D'$, which is defined in
59			Section~\ref{sec:othBG} to be the same as region $D$ but with the
60			$\met/\sqrt{\rm SumJetPt}$ requirement
61			replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement
62	claudioc	1.3	is $N_{D'}=1$. Thus the BG prediction is
63	benhoob	1.4	$N_D = K \cdot N_{D'} = 1.5$
64			where $K=1.5 \pm xx$ as derived in Sec.~\ref{sec:victory}.
65	claudioc	1.2	Note that if we were to subtract off from region $D'$
66	claudioc	1.3	the {\color{red} 0.4 $\pm$ 0.4} DY events estimated from
67			Section~\ref{sec:othBG}, the background
68			prediction would change to $N_D=0.9 \pm xx$ events.
69	benhoob	1.4
70			%%%TO BE REPLACED
71			%{\color{red}As mentioned previously, for the 11/pb analysis
72			%we use the $K$ factor from data and take $K=1$.
73			%This will change for the full dataset. We will also pay
74			%more attention to the statistical errors.}
75
76			%The number of data events in region $D'$, which is defined in
77			%Section~\ref{sec:othBG} to be the same as region $D$ but with the
78			%$\met/\sqrt{\rm SumJetPt}$ requirement
79			%replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement
80			%is $N_{D'}=1$. Thus the BG prediction is
81			%$N_D = K \cdot K_{\rm fudge} \cdot N_{D'} = 1.5$
82			%where we used $K=1.5 \pm xx$ and $K_{\rm fudge}=1.0 \pm 0.0$.
83			%Note that if we were to subtract off from region $D'$
84			%the {\color{red} 0.4 $\pm$ 0.4} DY events estimated from
85			%Section~\ref{sec:othBG}, the background
86			%prediction would change to $N_D=0.9 \pm xx$ events.
87			%{\color{red} When we do this with a real
88			%$K_{\rm fudge}$, the fudge factor will be different
89			%after the DY subtraction.}
90	claudioc	1.1
91	claudioc	1.2	As a cross-check, we use the $P_T(\ell \ell)$
92	claudioc	1.1	method to also predict the number of events in the
93			control region $120<{\rm SumJetPt}<300$ GeV and
94			\met/$\sqrt{\rm SumJetPt} > 8.5$. We predict
95			$5.6^{+x}_{-y}$ events and we observe 4.
96	claudioc	1.3	The results of the $P_T(\ell\ell)$ method are
97			summarized in Figure~\ref{fig:victory}.
98	claudioc	1.1
99	claudioc	1.3	\begin{figure}[hbt]
100			\begin{center}
101			\includegraphics[width=0.48\linewidth]{victory_control.png}
102			\includegraphics[width=0.48\linewidth]{victory_sig.png}
103			\caption{\label{fig:victory}\protect Distributions of
104			tcMet/$\sqrt{\rm SumJetPt}$ for the control and signal region.
105			We show the oberved distributions in both Monte Carlo and data.
106			We also show the distributions predicted from
107	benhoob	1.4	${P_T(\ell\ell)}/\sqrt{\rm SumJetPt}$ in both MC and data.}
108	claudioc	1.3	\end{center}
109			\end{figure}
110
111
112			\subsection{Summary of results}
113	claudioc	1.1	To summarize: we see no evidence for an anomalous
114			rate of opposite sign isolated dilepton events
115			at high \met and high SumJetPt. The extraction of
116			quantitative limits on new physics models is discussed
117	benhoob	1.5	in Section~\ref{sec:limit}.