| 23 |
|
|
| 24 |
|
We find that in $t\bar{t}$ events SumJetPt and |
| 25 |
|
\met$/\sqrt{\rm SumJetPt}$ are nearly uncorrelated, |
| 26 |
< |
as demonstrated in Figure~\ref{fig:uncor}. |
| 26 |
> |
as demonstrated in Fig.~\ref{fig:uncor}. |
| 27 |
|
Thus, we can use an ABCD method in the \met$/\sqrt{\rm SumJetPt}$ vs |
| 28 |
|
sumJetPt plane to estimate the background in a data driven way. |
| 29 |
|
|
| 48 |
|
|
| 49 |
|
\begin{figure}[tb] |
| 50 |
|
\begin{center} |
| 51 |
< |
\includegraphics[width=0.5\linewidth, angle=90]{abcdMC.pdf} |
| 51 |
> |
\includegraphics[width=0.75\linewidth]{ttdil_uncor_38X.png} |
| 52 |
|
\caption{\label{fig:abcdMC}\protect Distributions of MET$/\sqrt{\rm SumJetPt}$ vs. |
| 53 |
< |
SumJetPt for SM Monte Carlo. Here we also show our choice of ABCD regions.} |
| 53 |
> |
SumJetPt for SM Monte Carlo. Here we also show our choice of ABCD regions. The correlation coefficient |
| 54 |
> |
${\rm corr_{XY}}$ is computed for events falling in the ABCD regions.} |
| 55 |
|
\end{center} |
| 56 |
|
\end{figure} |
| 57 |
|
|
| 58 |
|
|
| 59 |
|
Our choice of ABCD regions is shown in Figure~\ref{fig:abcdMC}. |
| 60 |
|
The signal region is region D. The expected number of events |
| 61 |
< |
in the four regions for the SM Monte Carlo, as well as the BG |
| 62 |
< |
prediction AC/B are given in Table~\ref{tab:abcdMC} for an integrated |
| 61 |
> |
in the four regions for the SM Monte Carlo, as well as the background |
| 62 |
> |
prediction A $\times$ C / B are given in Table~\ref{tab:abcdMC} for an integrated |
| 63 |
|
luminosity of 35 pb$^{-1}$. The ABCD method with chosen boundaries is accurate |
| 64 |
< |
to about 20\%. As shown in Table~\ref{tab:abcdsyst}, we assess systematic uncertainties |
| 65 |
< |
by varying the boundaries by an amount consistent with the hadronic energy scale uncertainty, |
| 66 |
< |
which we take as $\pm$5\% for SumJetPt and $\pm$2.5\% for MET/$\sqrt{\rm SumJetPt}$, since the |
| 67 |
< |
uncertainty on this quantity partially cancels due to the fact that it is a ratio of correlated |
| 68 |
< |
quantities. Based on these studies we assess a correction factor $k_{ABCD} = 1.2 \pm 0.2$ to the |
| 69 |
< |
predicted yield using the ABCD method. |
| 64 |
> |
to about 20\%, and we assess a corresponding systematic uncertainty |
| 65 |
> |
{\bf \color{red} More detail needed here???} |
| 66 |
> |
|
| 67 |
> |
%As shown in Table~\ref{tab:abcdsyst}, we assess systematic uncertainties |
| 68 |
> |
%by varying the boundaries by an amount consistent with the hadronic energy scale uncertainty, |
| 69 |
> |
%which we take as $\pm$5\% for SumJetPt and $\pm$2.5\% for MET/$\sqrt{\rm SumJetPt}$, since the |
| 70 |
> |
%uncertainty on this quantity partially cancels due to the fact that it is a ratio of correlated |
| 71 |
> |
%quantities. Based on these studies we assess a correction factor $k_{ABCD} = 1.2 \pm 0.2$ to the |
| 72 |
> |
%predicted yield using the ABCD method. |
| 73 |
|
|
| 74 |
|
|
| 75 |
|
%{\color{red} Avi wants some statement about stability |
| 146 |
|
|
| 147 |
|
\newcommand{\ptll} {\ensuremath{P_T(\ell\ell)}} |
| 148 |
|
\begin{center} |
| 149 |
< |
$ K = \frac{\int_0^{\infty} {\cal N}(\ptll)~~d\ptll~}{\int_{50}^{\infty} {\cal N}(\ptll)~~d\ptll~}$ |
| 149 |
> |
$ K = \frac{\int_0^{\infty} {\cal N}(\ptll)~~d\ptll~}{\int_{50}^{\infty} {\cal N}(\ptll)~~d\ptll~} = 1.52$ |
| 150 |
|
\end{center} |
| 151 |
|
|
| 152 |
|
|
| 149 |
– |
Monte Carlo studies give values of $K$ that are typically between 1.5 and 1.6, |
| 150 |
– |
depending on selection details. |
| 153 |
|
%%%TO BE REPLACED |
| 154 |
|
%Given the integrated luminosity of the |
| 155 |
|
%present dataset, the determination of $K$ in data is severely statistics |
| 197 |
|
\begin{table}[htb] |
| 198 |
|
\begin{center} |
| 199 |
|
\caption{\label{tab:victorybad} |
| 200 |
< |
{\bf \color{red} Need to either update this with 38X MC, or replace it with the systematic studies varying the non-ttdil background yield and jet/met scale. } |
| 200 |
> |
{\bf \color{red} Need to either update this with 38X MC or remove it } |
| 201 |
|
Test of the data driven method in Monte Carlo |
| 202 |
|
under different assumptions. See text for details.} |
| 203 |
|
\begin{tabular}{|l|c|c|c|c|c|c|c|c|} |
| 204 |
|
\hline |
| 205 |
|
& True $t\bar{t}$ dilepton & $t\to W\to\tau$& other SM & GEN or & Lepton $P_T$ & Z veto & \met $>$ 50& obs/pred \\ |
| 206 |
< |
& included & included & included & RECOSIM & and $\eta$ cuts & & & \\ \hline |
| 206 |
> |
& included & included & included & RECOSIM & and $\eta$ cuts & & & \\ \hline |
| 207 |
|
1&Y & N & N & GEN & N & N & N & 1.90 \\ |
| 208 |
|
2&Y & N & N & GEN & Y & N & N & 1.64 \\ |
| 209 |
|
3&Y & N & N & GEN & Y & Y & N & 1.59 \\ |
| 211 |
|
5&Y & N & N & RECOSIM & Y & Y & Y & 1.51 \\ |
| 212 |
|
6&Y & Y & N & RECOSIM & Y & Y & Y & 1.58 \\ |
| 213 |
|
7&Y & Y & Y & RECOSIM & Y & Y & Y & 1.38 \\ |
| 212 |
– |
%%%NOTE: updated value 1.18 -> 1.46 since 2/3 DY events have been removed by updated analysis selections, |
| 213 |
– |
%%%dpt/pt cut and general lepton veto |
| 214 |
|
\hline |
| 215 |
|
\end{tabular} |
| 216 |
|
\end{center} |
| 217 |
|
\end{table} |
| 218 |
|
|
| 219 |
|
|
| 220 |
+ |
\begin{table}[htb] |
| 221 |
+ |
\begin{center} |
| 222 |
+ |
\caption{\label{tab:victorysyst} |
| 223 |
+ |
Summary of uncertainties in $K_C$ due to the MET scale and resolution uncertainty, and to backgrounds other than $t\bar{t} \to$~dilepton. |
| 224 |
+ |
In the first table, `up' and `down' refer to shifting the hadronic energy scale up and down by 5\%. In the second table, the quoted value |
| 225 |
+ |
refers to the amount of additional smearing of the MET, as discussed in the text. In the third table, the normalization of all backgrounds |
| 226 |
+ |
other than $t\bar{t} \to$~dilepton is varied. |
| 227 |
+ |
{\bf \color{red} Should I remove `observed' and `predicted' and show only the ratio? }} |
| 228 |
+ |
|
| 229 |
+ |
\begin{tabular}{ lcccc } |
| 230 |
+ |
\hline |
| 231 |
+ |
MET scale & Predicted & Observed & Obs/pred \\ |
| 232 |
+ |
\hline |
| 233 |
+ |
nominal & 0.92 $ \pm $ 0.11 & 1.29 $ \pm $ 0.11 & 1.40 $ \pm $ 0.20 \\ |
| 234 |
+ |
up & 0.92 $ \pm $ 0.11 & 1.53 $ \pm $ 0.12 & 1.66 $ \pm $ 0.23 \\ |
| 235 |
+ |
down & 0.81 $ \pm $ 0.07 & 1.08 $ \pm $ 0.11 & 1.32 $ \pm $ 0.17 \\ |
| 236 |
+ |
\hline |
| 237 |
+ |
MET smearing & Predicted & Observed & Obs/pred \\ |
| 238 |
+ |
\hline |
| 239 |
+ |
nominal & 0.92 $ \pm $ 0.11 & 1.29 $ \pm $ 0.11 & 1.40 $ \pm $ 0.20 \\ |
| 240 |
+ |
10\% & 0.90 $ \pm $ 0.11 & 1.30 $ \pm $ 0.11 & 1.44 $ \pm $ 0.21 \\ |
| 241 |
+ |
20\% & 0.84 $ \pm $ 0.07 & 1.36 $ \pm $ 0.11 & 1.61 $ \pm $ 0.19 \\ |
| 242 |
+ |
30\% & 1.05 $ \pm $ 0.18 & 1.32 $ \pm $ 0.11 & 1.27 $ \pm $ 0.24 \\ |
| 243 |
+ |
40\% & 0.85 $ \pm $ 0.07 & 1.37 $ \pm $ 0.11 & 1.61 $ \pm $ 0.19 \\ |
| 244 |
+ |
50\% & 1.08 $ \pm $ 0.18 & 1.36 $ \pm $ 0.11 & 1.26 $ \pm $ 0.24 \\ |
| 245 |
+ |
\hline |
| 246 |
+ |
non-$t\bar{t} \to$~dilepton scale factor & Predicted & Observed & Obs/pred \\ |
| 247 |
+ |
\hline |
| 248 |
+ |
ttdil only & 0.77 $ \pm $ 0.07 & 1.05 $ \pm $ 0.06 & 1.36 $ \pm $ 0.14 \\ |
| 249 |
+ |
nominal & 0.92 $ \pm $ 0.11 & 1.29 $ \pm $ 0.11 & 1.40 $ \pm $ 0.20 \\ |
| 250 |
+ |
double non-ttdil yield & 1.06 $ \pm $ 0.18 & 1.52 $ \pm $ 0.20 & 1.43 $ \pm $ 0.30 \\ |
| 251 |
+ |
\hline |
| 252 |
+ |
\end{tabular} |
| 253 |
+ |
\end{center} |
| 254 |
+ |
\end{table} |
| 255 |
+ |
|
| 256 |
+ |
|
| 257 |
+ |
|
| 258 |
|
The largest discrepancy between prediction and observation occurs on the first |
| 259 |
|
line of Table~\ref{tab:victorybad}, {\em i.e.}, at the generator level with no |
| 260 |
|
cuts. We have verified that this effect is due to the polarization of |
| 277 |
|
We have studied this effect at the generator level using Alpgen. We find |
| 278 |
|
that the bias is at the few percent level. |
| 279 |
|
|
| 280 |
< |
%%%TO BE REPLACED |
| 281 |
< |
%Based on the results of Table~\ref{tab:victorybad}, we conclude that the |
| 282 |
< |
%naive data driven background estimate based on $P_T{(\ell\ell)}$ needs to |
| 283 |
< |
%be corrected by a factor of {\color{red} $ K_{\rm{fudge}} =1.2 \pm 0.3$ |
| 284 |
< |
%(We still need to settle on thie exact value of this. |
| 285 |
< |
%For the 11 pb analysis it is taken as =1.)} . The quoted |
| 286 |
< |
%uncertainty is based on the stability of the Monte Carlo tests under |
| 287 |
< |
%variations of event selections, choices of \met algorithm, etc. |
| 288 |
< |
%For example, we find that observed/predicted changes by roughly 0.1 |
| 289 |
< |
%for each 1.5\% change in the average \met response. |
| 290 |
< |
|
| 291 |
< |
Based on the results of Table~\ref{tab:victorybad}, we conclude that the |
| 292 |
< |
naive data driven background estimate based on $P_T{(\ell\ell)}$ needs to |
| 293 |
< |
be corrected by a factor of $ K_C = X \pm Y$. |
| 294 |
< |
The value of this correction factor as well as the systematic uncertainty |
| 295 |
< |
will be assessed using 38X ttbar madgraph MC. In the following we use |
| 296 |
< |
$K_C = 1$ for simplicity. Based on previous MC studies we foresee a correction |
| 259 |
< |
factor of $K_C \approx 1.2 - 1.5$, and we will assess an uncertainty |
| 260 |
< |
based on the stability of the Monte Carlo tests under |
| 261 |
< |
variations of event selections, choices of \met algorithm, etc. |
| 262 |
< |
For example, we find that observed/predicted changes by roughly 0.1 |
| 263 |
< |
for each 1.5\% change in the average \met response. |
| 264 |
< |
|
| 280 |
> |
Based on the results of Table~\ref{tab:victorysyst}, we conclude that the |
| 281 |
> |
naive data-driven background estimate based on $P_T{(\ell\ell)}$ needs to |
| 282 |
> |
be corrected by a factor of $ K_C = 1.4 \pm 0.2({\rm stat})$. |
| 283 |
> |
|
| 284 |
> |
The dominant sources of systematic uncertainty in $K_C$ are due to non-$t\bar{t} \to$~dilepton backgrounds, |
| 285 |
> |
and the MET scale and resolution uncertainties, as summarized in Table~\ref{tab:victorysyst}. |
| 286 |
> |
The impact of non-$t\bar{t}$-dilepton background is assessed |
| 287 |
> |
by varying the yield of all backgrounds except for $t\bar{t} \to$~dilepton. |
| 288 |
> |
The uncertainty is assessed as the larger of the differences between the nominal $K_C$ value and the values |
| 289 |
> |
obtained using only $t\bar{t} \to$~dilepton MC and obtained by doubling the non $t\bar{t} \to$~dilepton component, |
| 290 |
> |
giving an uncertainty of $0.04$. |
| 291 |
> |
|
| 292 |
> |
The uncertainty in $K_C$ due to the MET scale uncertainty is assessed by varying the hadronic energy scale using |
| 293 |
> |
the same method as in~\ref{ref:top}, giving an uncertainty of 0.3. We also assess the impact of the MET resolution |
| 294 |
> |
uncertainty on $K_C$ by applying a random smearing to the MET. For each event, we determine the expected MET resolution |
| 295 |
> |
based on the sumJetPt, and smear the MET to simulate an increase in the resolution of 10\%, 20\%, 30\%, 40\% and 50\%. |
| 296 |
> |
The results show that $K_C$ does not depend strongly on the MET resolution and we therefore do not assess any uncertainty. |
| 297 |
|
|
| 298 |
+ |
Incorporating all the statistical and systematic uncertainties we find $K_C = 1.4 \pm 0.4$. |
| 299 |
|
|
| 300 |
|
\subsection{Signal Contamination} |
| 301 |
|
\label{sec:sigcont} |
