| 54 |
|
in the four regions for the SM Monte Carlo, as well as the BG |
| 55 |
|
prediction AC/B are given in Table~\ref{tab:abcdMC} for an integrated |
| 56 |
|
luminosity of 30 pb$^{-1}$. The ABCD method is accurate |
| 57 |
< |
to about 10\%. {\color{red} Avi wants some statement about stability |
| 58 |
< |
wrt changes in regions. I am not sure that we have done it and |
| 59 |
< |
I am not sure it is necessary (Claudio).} |
| 57 |
> |
to about 10\%. |
| 58 |
> |
%{\color{red} Avi wants some statement about stability |
| 59 |
> |
%wrt changes in regions. I am not sure that we have done it and |
| 60 |
> |
%I am not sure it is necessary (Claudio).} |
| 61 |
|
|
| 62 |
|
\begin{table}[htb] |
| 63 |
|
\begin{center} |
| 99 |
|
|
| 100 |
|
|
| 101 |
|
Monte Carlo studies give values of $K$ that are typically between 1.5 and 1.6, |
| 102 |
< |
depending on selection details. |
| 102 |
> |
depending on selection details. Given the integrated luminosity of the |
| 103 |
> |
present dataset, the determination of $K$ in data is severely statistics |
| 104 |
> |
limited. Thus, we take $K$ from $t\bar{t}$ Monte Carlo as |
| 105 |
> |
|
| 106 |
> |
\begin{center} |
| 107 |
> |
$ K_{MC} = \frac{\int_0^{\infty} {\cal N}(\met)~~d\met~}{\int_{50}^{\infty} {\cal N}(\met)~~d\met~}$ |
| 108 |
> |
\end{center} |
| 109 |
> |
|
| 110 |
> |
\noindent {\color{red} For the 11 pb result we have used $K$ from data.} |
| 111 |
|
|
| 112 |
|
There are several effects that spoil the correspondance between \met and |
| 113 |
|
$P_T(\ell\ell)$: |
| 183 |
|
|
| 184 |
|
Based on the results of Table~\ref{tab:victorybad}, we conclude that the |
| 185 |
|
naive data driven background estimate based on $P_T{(\ell\ell)}$ needs to |
| 186 |
< |
be corrected by a factor of {\color{red} $1.2 \pm 0.3$ (We need to talk |
| 187 |
< |
about this)} . The quoted |
| 186 |
> |
be corrected by a factor of {\color{red} $ K_{\rm{fudge}} =1.2 \pm 0.3$ |
| 187 |
> |
(We still need to settle on thie exact value of this. |
| 188 |
> |
For the 11 pb analysis it is taken as =1.)} . The quoted |
| 189 |
|
uncertainty is based on the stability of the Monte Carlo tests under |
| 190 |
|
variations of event selections, choices of \met algorithm, etc. |
| 191 |
< |
For example, We find that observed/predicted changes by roughly 0.1 |
| 191 |
> |
For example, we find that observed/predicted changes by roughly 0.1 |
| 192 |
|
for each 1.5\% change in the average \met response. |
| 193 |
|
|
| 194 |
|
|
| 229 |
|
are normalized to 30 pb$^{-1}$.} |
| 230 |
|
\begin{tabular}{|c||c|c||c|c|} |
| 231 |
|
\hline |
| 232 |
< |
SM & SM$+$LM0 & BG Prediction & Sm$+$LM1 & BG Prediction \\ |
| 232 |
> |
SM & SM$+$LM0 & BG Prediction & SM$+$LM1 & BG Prediction \\ |
| 233 |
|
Background & Contribution& Including LM0 & Contribution & Including LM1 \\ \hline |
| 234 |
|
1.2 & 6.8 & 3.7 & 3.4 & 1.3 \\ |
| 235 |
|
\hline |
| 242 |
|
\caption{\label{tab:sigcontPT} Effects of signal contamination |
| 243 |
|
for the background predictions of the $P_T(\ell\ell)$ method including LM0 or |
| 244 |
|
LM1. Results |
| 245 |
< |
are normalized to 30 pb$^{-1}$. {\color{red} Does this BG prediction include |
| 236 |
< |
the fudge factor of 1.4 or watever because the method is not perfect.}} |
| 245 |
> |
are normalized to 30 pb$^{-1}$.} |
| 246 |
|
\begin{tabular}{|c||c|c||c|c|} |
| 247 |
|
\hline |
| 248 |
< |
SM & SM$+$LM0 & BG Prediction & Sm$+$LM1 & BG Prediction \\ |
| 248 |
> |
SM & SM$+$LM0 & BG Prediction & SM$+$LM1 & BG Prediction \\ |
| 249 |
|
Background & Contribution& Including LM0 & Contribution & Including LM1 \\ \hline |
| 250 |
|
1.2 & 6.8 & 2.2 & 3.4 & 1.5 \\ |
| 251 |
|
\hline |
