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\label{sec:datadriven} |
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We have developed two data-driven methods to |
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estimate the background in the signal region. |
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The first one explouts the fact that |
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> |
The first one exploits the fact that |
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\met and \met$/\sqrt{\rm SumJetPt}$ are nearly |
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uncorrelated for the $t\bar{t}$ background |
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(Section~\ref{sec:abcd}); the second one |
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from $W$-decays, which is reconstructed as \met in the |
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detector. |
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|
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In 30 pb$^{-1}$ we expect $\approx$ 1 SM event in |
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the signal region. The expectations from the LMO |
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and LM1 SUSY benchmark points are 5.6 and |
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2.2 events respectively. |
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|
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%{\color{red} I took these |
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%numbers from the twiki, rescaling from 11.06 to 30/pb. |
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%They seem too large...are they really right?} |
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\label{sec:abcd} |
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|
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We find that in $t\bar{t}$ events \met and |
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\met$/\sqrt{\rm SumJetPt}$ are nearly uncorrelated. |
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This is demonstrated in Figure~\ref{fig:uncor}. |
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\met$/\sqrt{\rm SumJetPt}$ are nearly uncorrelated, |
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as demonstrated in Figure~\ref{fig:uncor}. |
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Thus, we can use an ABCD method in the \met$/\sqrt{\rm SumJetPt}$ vs |
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sumJetPt plane to estimate the background in a data driven way. |
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|
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The signal region is region D. The expected number of events |
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in the four regions for the SM Monte Carlo, as well as the BG |
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prediction AC/B are given in Table~\ref{tab:abcdMC} for an integrated |
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luminosity of 30 pb$^{-1}$. The ABCD method is accurate |
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to about 10\%. {\color{red} Avi wants some statement about stability |
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wrt changes in regions. I am not sure that we have done it and |
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I am not sure it is necessary (Claudio).} |
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luminosity of 35 pb$^{-1}$. The ABCD method is accurate |
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> |
to about 20\%. |
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> |
%{\color{red} Avi wants some statement about stability |
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> |
%wrt changes in regions. I am not sure that we have done it and |
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%I am not sure it is necessary (Claudio).} |
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|
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\begin{table}[htb] |
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\begin{center} |
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\caption{\label{tab:abcdMC} Expected SM Monte Carlo yields for |
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< |
30 pb$^{-1}$ in the ABCD regions.} |
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< |
\begin{tabular}{|l|c|c|c|c||c|} |
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> |
35 pb$^{-1}$ in the ABCD regions, as well as the predicted yield in |
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> |
the signal region given by A $\times$ C / B. Here `SM other' is the sum |
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> |
of non-dileptonic $t\bar{t}$ decays, $W^{\pm}$+jets, $W^+W^-$, |
| 65 |
> |
$W^{\pm}Z^0$, $Z^0Z^0$ and single top.} |
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> |
\begin{tabular}{lccccc} |
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> |
\hline |
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> |
sample & A & B & C & D & A $\times$ C / B \\ |
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> |
\hline |
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> |
$t\bar{t}\rightarrow \ell^{+}\ell^{-}$ & 7.96 & 33.07 & 4.81 & 1.20 & 1.16 \\ |
| 71 |
> |
$Z^0$ + jets & 0.00 & 1.16 & 0.08 & 0.08 & 0.00 \\ |
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> |
SM other & 0.65 & 2.31 & 0.17 & 0.14 & 0.05 \\ |
| 73 |
> |
\hline |
| 74 |
> |
total SM MC & 8.61 & 36.54 & 5.05 & 1.41 & 1.19 \\ |
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|
\hline |
| 67 |
– |
Sample & A & B & C & D & AC/D \\ \hline |
| 68 |
– |
ttdil & 6.9 & 28.6 & 4.2 & 1.0 & 1.0 \\ |
| 69 |
– |
Zjets & 0.0 & 1.3 & 0.1 & 0.1 & 0.0 \\ |
| 70 |
– |
Other SM & 0.5 & 2.0 & 0.1 & 0.1 & 0.0 \\ \hline |
| 71 |
– |
total MC & 7.4 & 31.9 & 4.4 & 1.2 & 1.0 \\ \hline |
| 76 |
|
\end{tabular} |
| 77 |
|
\end{center} |
| 78 |
|
\end{table} |
| 93 |
|
to account for the fact that any dilepton selection must include a |
| 94 |
|
moderate \met cut in order to reduce Drell Yan backgrounds. This |
| 95 |
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is discussed in Section 5.3 of Reference~\cite{ref:ourvictory}; for a \met |
| 96 |
< |
cut of 50 GeV, the rescaling factor is obtained from the data as |
| 96 |
> |
cut of 50 GeV, the rescaling factor is obtained from the MC as |
| 97 |
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|
| 98 |
|
\newcommand{\ptll} {\ensuremath{P_T(\ell\ell)}} |
| 99 |
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\begin{center} |
| 102 |
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|
| 103 |
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|
| 104 |
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Monte Carlo studies give values of $K$ that are typically between 1.5 and 1.6, |
| 105 |
< |
depending on selection details. |
| 105 |
> |
depending on selection details. |
| 106 |
> |
%%%TO BE REPLACED |
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> |
%Given the integrated luminosity of the |
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> |
%present dataset, the determination of $K$ in data is severely statistics |
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> |
%limited. Thus, we take $K$ from $t\bar{t}$ Monte Carlo as |
| 110 |
> |
|
| 111 |
> |
%\begin{center} |
| 112 |
> |
%$ K_{MC} = \frac{\int_0^{\infty} {\cal N}(\met)~~d\met~}{\int_{50}^{\infty} {\cal N}(\met)~~d\met~}$ |
| 113 |
> |
%\end{center} |
| 114 |
> |
|
| 115 |
> |
%\noindent {\color{red} For the 11 pb result we have used $K$ from data.} |
| 116 |
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|
| 117 |
|
There are several effects that spoil the correspondance between \met and |
| 118 |
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$P_T(\ell\ell)$: |
| 125 |
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\item Similarly, the \met$>$50 GeV cut introduces an asymmetry between leptons and |
| 126 |
|
neutrinos which is only partially compensated by the $K$ factor above. |
| 127 |
|
\item The \met resolution is much worse than the dilepton $P_T$ resolution. |
| 128 |
< |
When convoluted with a falling spectrum in the tails of \met, this result |
| 128 |
> |
When convoluted with a falling spectrum in the tails of \met, this results |
| 129 |
|
in a harder spectrum for \met than the original $P_T(\nu\nu)$. |
| 130 |
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\item The \met response in CMS is not exactly 1. This causes a distortion |
| 131 |
|
in the \met distribution that is not present in the $P_T(\ell\ell)$ distribution. |
| 136 |
|
sources. These events can affect the background prediction. Particularly |
| 137 |
|
dangerous are high $P_T$ Drell Yan events that barely pass the \met$>$ 50 |
| 138 |
|
GeV selection. They will tend to push the data-driven background prediction up. |
| 139 |
+ |
Therefore we estimate the number of DY events entering the background prediction |
| 140 |
+ |
using the $R_{out/in}$ method as described in Sec.~\ref{sec:othBG}. |
| 141 |
|
\end{itemize} |
| 142 |
|
|
| 143 |
|
We have studied these effects in SM Monte Carlo, using a mixture of generator and |
| 160 |
|
4&Y & N & N & GEN & Y & Y & Y & 1.55 \\ |
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|
5&Y & N & N & RECOSIM & Y & Y & Y & 1.51 \\ |
| 162 |
|
6&Y & Y & N & RECOSIM & Y & Y & Y & 1.58 \\ |
| 163 |
< |
7&Y & Y & Y & RECOSIM & Y & Y & Y & 1.18 \\ |
| 163 |
> |
7&Y & Y & Y & RECOSIM & Y & Y & Y & 1.46 \\ |
| 164 |
> |
%%%NOTE: updated value 1.18 -> 1.46 since 2/3 DY events have been removed by updated analysis selections, |
| 165 |
> |
%%%dpt/pt cut and general lepton veto |
| 166 |
|
\hline |
| 167 |
|
\end{tabular} |
| 168 |
|
\end{center} |
| 180 |
|
% by $\approx 4\%$\footnote{We find that observed/predicted changes by roughly 0.1 |
| 181 |
|
%for each 1.5\% change in \met response.}. |
| 182 |
|
Finally, contamination from non $t\bar{t}$ |
| 183 |
< |
events can have a significant impact on the BG prediction. The changes between |
| 184 |
< |
lines 6 and 7 of Table~\ref{tab:victorybad} is driven by 3 |
| 185 |
< |
Drell Yan events that pass the \met selection in Monte Carlo (thus the effect |
| 186 |
< |
is statistically not well quantified). |
| 183 |
> |
events can have a significant impact on the BG prediction. |
| 184 |
> |
%The changes between |
| 185 |
> |
%lines 6 and 7 of Table~\ref{tab:victorybad} is driven by 3 |
| 186 |
> |
%Drell Yan events that pass the \met selection in Monte Carlo (thus the effect |
| 187 |
> |
%is statistically not well quantified). |
| 188 |
|
|
| 189 |
|
An additional source of concern is that the CMS Madgraph $t\bar{t}$ MC does |
| 190 |
|
not include effects of spin correlations between the two top quarks. |
| 191 |
|
We have studied this effect at the generator level using Alpgen. We find |
| 192 |
< |
that the bias is a the few percent level. |
| 192 |
> |
that the bias is at the few percent level. |
| 193 |
> |
|
| 194 |
> |
%%%TO BE REPLACED |
| 195 |
> |
%Based on the results of Table~\ref{tab:victorybad}, we conclude that the |
| 196 |
> |
%naive data driven background estimate based on $P_T{(\ell\ell)}$ needs to |
| 197 |
> |
%be corrected by a factor of {\color{red} $ K_{\rm{fudge}} =1.2 \pm 0.3$ |
| 198 |
> |
%(We still need to settle on thie exact value of this. |
| 199 |
> |
%For the 11 pb analysis it is taken as =1.)} . The quoted |
| 200 |
> |
%uncertainty is based on the stability of the Monte Carlo tests under |
| 201 |
> |
%variations of event selections, choices of \met algorithm, etc. |
| 202 |
> |
%For example, we find that observed/predicted changes by roughly 0.1 |
| 203 |
> |
%for each 1.5\% change in the average \met response. |
| 204 |
|
|
| 205 |
|
Based on the results of Table~\ref{tab:victorybad}, we conclude that the |
| 206 |
|
naive data driven background estimate based on $P_T{(\ell\ell)}$ needs to |
| 207 |
< |
be corrected by a factor of {\color{red} $1.2 \pm 0.3$ (We need to talk |
| 208 |
< |
about this)} . The quoted |
| 209 |
< |
uncertainty is based on the stability of the Monte Carlo tests under |
| 207 |
> |
be corrected by a factor of $ K_C = X \pm Y$. |
| 208 |
> |
The value of this correction factor as well as the systematic uncertainty |
| 209 |
> |
will be assessed using 38X ttbar madgraph MC. In the following we use |
| 210 |
> |
$K_C = 1$ for simplicity. Based on previous MC studies we foresee a correction |
| 211 |
> |
factor of $K_C \approx 1.2 - 1.5$, and we will assess an uncertainty |
| 212 |
> |
based on the stability of the Monte Carlo tests under |
| 213 |
|
variations of event selections, choices of \met algorithm, etc. |
| 214 |
+ |
For example, we find that observed/predicted changes by roughly 0.1 |
| 215 |
+ |
for each 1.5\% change in the average \met response. |
| 216 |
|
|
| 217 |
|
|
| 218 |
|
|
| 238 |
|
|
| 239 |
|
The LM points are benchmarks for SUSY analyses at CMS. The effects |
| 240 |
|
of signal contaminations for a couple such points are summarized |
| 241 |
< |
in Table~\ref{tab:sigcontABCD} and~\ref{tab:sigcontPT}. |
| 207 |
< |
Signal contamination is definitely an important |
| 241 |
> |
in Table~\ref{tab:sigcont}. Signal contamination is definitely an important |
| 242 |
|
effect for these two LM points, but it does not totally hide the |
| 243 |
|
presence of the signal. |
| 244 |
|
|
| 245 |
|
|
| 246 |
|
\begin{table}[htb] |
| 247 |
|
\begin{center} |
| 248 |
< |
\caption{\label{tab:sigcontABCD} Effects of signal contamination |
| 249 |
< |
for the background predictions of the ABCD method including LM0 or |
| 250 |
< |
LM1. Results |
| 251 |
< |
are normalized to 30 pb$^{-1}$.} |
| 252 |
< |
\begin{tabular}{|c||c|c||c|c|} |
| 219 |
< |
\hline |
| 220 |
< |
SM & LM0 & BG Prediction & LM1 & BG Prediction \\ |
| 221 |
< |
Background & Contribution& Including LM0 & Contribution & Including LM1 \\ \hline |
| 222 |
< |
1.2 & 5.6 & 3.7 & 2.2 & 1.3 \\ |
| 248 |
> |
\caption{\label{tab:sigcont} Effects of signal contamination |
| 249 |
> |
for the two data-driven background estimates. The three columns give |
| 250 |
> |
the expected yield in the signal region and the background estimates |
| 251 |
> |
using the ABCD and $P_T(\ell \ell)$ methods. Results are normalized to 35~pb$^{-1}$.} |
| 252 |
> |
\begin{tabular}{lccc} |
| 253 |
|
\hline |
| 254 |
< |
\end{tabular} |
| 255 |
< |
\end{center} |
| 256 |
< |
\end{table} |
| 257 |
< |
|
| 258 |
< |
\begin{table}[htb] |
| 229 |
< |
\begin{center} |
| 230 |
< |
\caption{\label{tab:sigcontPT} Effects of signal contamination |
| 231 |
< |
for the background predictions of the $P_T(\ell\ell)$ method including LM0 or |
| 232 |
< |
LM1. Results |
| 233 |
< |
are normalized to 30 pb$^{-1}$. {\color{red} Does this BG prediction include |
| 234 |
< |
the fudge factor of 1.4 or watever because the method is not perfect.}} |
| 235 |
< |
\begin{tabular}{|c||c|c||c|c|} |
| 236 |
< |
\hline |
| 237 |
< |
SM & LM0 & BG Prediction & LM1 & BG Prediction \\ |
| 238 |
< |
Background & Contribution& Including LM0 & Contribution & Including LM1 \\ \hline |
| 239 |
< |
1.2 & 5.6 & 2.2 & 2.2 & 1.5 \\ |
| 254 |
> |
& Yield & ABCD & $P_T(\ell \ell)$ \\ |
| 255 |
> |
\hline |
| 256 |
> |
SM only & 1.41 & 1.19 & 0.96 \\ |
| 257 |
> |
SM + LM0 & 7.88 & 4.24 & 2.28 \\ |
| 258 |
> |
SM + LM1 & 3.98 & 1.53 & 1.44 \\ |
| 259 |
|
\hline |
| 260 |
|
\end{tabular} |
| 261 |
|
\end{center} |
| 262 |
|
\end{table} |
| 263 |
|
|
| 264 |
+ |
|
| 265 |
+ |
|
| 266 |
+ |
%\begin{table}[htb] |
| 267 |
+ |
%\begin{center} |
| 268 |
+ |
%\caption{\label{tab:sigcontABCD} Effects of signal contamination |
| 269 |
+ |
%for the background predictions of the ABCD method including LM0 or |
| 270 |
+ |
%LM1. Results |
| 271 |
+ |
%are normalized to 30 pb$^{-1}$.} |
| 272 |
+ |
%\begin{tabular}{|c|c||c|c||c|c|} |
| 273 |
+ |
%\hline |
| 274 |
+ |
%SM & BG Prediction & SM$+$LM0 & BG Prediction & SM$+$LM1 & BG Prediction \\ |
| 275 |
+ |
%Background & SM Only & Contribution & Including LM0 & Contribution & Including LM1 \\ \hline |
| 276 |
+ |
%1.2 & 1.0 & 6.8 & 3.7 & 3.4 & 1.3 \\ |
| 277 |
+ |
%\hline |
| 278 |
+ |
%\end{tabular} |
| 279 |
+ |
%\end{center} |
| 280 |
+ |
%\end{table} |
| 281 |
+ |
|
| 282 |
+ |
%\begin{table}[htb] |
| 283 |
+ |
%\begin{center} |
| 284 |
+ |
%\caption{\label{tab:sigcontPT} Effects of signal contamination |
| 285 |
+ |
%for the background predictions of the $P_T(\ell\ell)$ method including LM0 or |
| 286 |
+ |
%LM1. Results |
| 287 |
+ |
%are normalized to 30 pb$^{-1}$.} |
| 288 |
+ |
%\begin{tabular}{|c|c||c|c||c|c|} |
| 289 |
+ |
%\hline |
| 290 |
+ |
%SM & BG Prediction & SM$+$LM0 & BG Prediction & SM$+$LM1 & BG Prediction \\ |
| 291 |
+ |
%Background & SM Only & Contribution & Including LM0 & Contribution & Including LM1 \\ \hline |
| 292 |
+ |
%1.2 & 1.0 & 6.8 & 2.2 & 3.4 & 1.5 \\ |
| 293 |
+ |
%\hline |
| 294 |
+ |
%\end{tabular} |
| 295 |
+ |
%\end{center} |
| 296 |
+ |
%\end{table} |
| 297 |
+ |
|
