| 1 |
|
\section{Counting Experiments} |
| 2 |
|
\label{sec:datadriven} |
| 3 |
|
|
| 4 |
< |
To look for possible BSM contributions, we define 2 signal regions that preserve about |
| 4 |
> |
To look for possible BSM contributions, we define 2 signal regions that reject all but |
| 5 |
|
0.1\% of the dilepton $t\bar{t}$ events, by adding requirements of large \MET\ and \Ht: |
| 6 |
|
|
| 7 |
|
\begin{itemize} |
| 13 |
|
dominated by dilepton $t\bar{t}$; the expected LM1 yield is 17 (14) and the |
| 14 |
|
expected LM3 yield is 6.4 (6.7). The signal regions are indicated in Fig.~\ref{fig:met_ht}. |
| 15 |
|
These signal regions are tighter than the one used in our published 2010 analysis since |
| 16 |
< |
with the larger data sample they give improved sensitivity to contributions from new physics. |
| 16 |
> |
with the larger data sample they allow us to explore phase space farther from the core |
| 17 |
> |
of the SM distributions. |
| 18 |
> |
|
| 19 |
|
|
| 20 |
|
We perform counting experiments in these signal regions, and use three independent methods to estimate from data the background in the signal region. |
| 21 |
< |
The first method is a novel technique based on the ABCD method, which we used in our 2010 analysis~\cite{ref:ospaper}, |
| 21 |
> |
The first method is a novel technique which is a variation of the ABCD method, which we used in our 2010 analysis~\cite{ref:ospaper}, |
| 22 |
|
and exploits the fact that \HT\ and $y \equiv \MET/\sqrt{H_T}$ are nearly uncorrelated for the $t\bar{t}$ background; |
| 23 |
|
this method is referred to as the ABCD' technique. First, we extract the $y$ and \Ht\ distributions |
| 24 |
|
$f(y)$ and $g(H_T)$ from data, using events from control regions which are dominated by background. |
| 41 |
|
%to measure $f(y)$ and $g(H_T)$. We then |
| 42 |
|
%multiply this ratio by the number events which fall in the control region in data |
| 43 |
|
%to get the predicted yield, ie. $N_{pred} = R_{S/C} \times N({\rm control})$. |
| 44 |
< |
To estimate the statistical uncertainty in the predicted background, the bin contents |
| 45 |
< |
of $f(y)$ and $g(H_T)$ are smeared according to their Poisson uncertainties, the prediction is repeated 20 times |
| 46 |
< |
with these smeared distributions, and the RMS of the deviation from the nominal prediction is taken |
| 45 |
< |
as the statistical uncertainty. We have studied this technique using toy MC studies based on |
| 44 |
> |
To estimate the statistical uncertainty in the predicted background, the bin contents |
| 45 |
> |
of $f(y)$ and $g(H_T)$ are smeared according to their Poisson uncertainties. |
| 46 |
> |
We have studied this technique using toy MC studies based on |
| 47 |
|
event samples of similar size to the expected yield in data for 1 fb$^{-1}$. |
| 48 |
|
Based on these studies we correct the predicted background yields by factors of 1.2 $\pm$ 0.5 |
| 49 |
|
(1.0 $\pm$ 0.5) for the high \MET\ (high \Ht) signal region. |
| 70 |
|
|
| 71 |
|
Our third background estimation method is based on the fact that many models of new physics |
| 72 |
|
produce an excess of SF with respect to OF lepton pairs, while for the \ttbar\ background the |
| 73 |
< |
rates of SF and OF lepton pairs are the same. Hence we make use of the OF subtraction technique |
| 73 |
< |
discussed in Sec.~\ref{sec:fit} in which we performed a shape analysis of the dilepton mass distribution. |
| 73 |
> |
rates of SF and OF lepton pairs are the same, as discussed in Sec.~\ref{sec:fit}. |
| 74 |
|
Here we perform a counting experiment, by quantifying the excess of SF vs. OF pairs using the |
| 75 |
|
quantity |
| 76 |
|
|
