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|
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We find that in $t\bar{t}$ events SumJetPt and |
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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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> |
as demonstrated in Fig.~\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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|
48 |
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|
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\begin{figure}[tb] |
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\begin{center} |
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< |
\includegraphics[width=0.5\linewidth, angle=90]{abcdMC.pdf} |
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> |
\includegraphics[width=0.75\linewidth]{ttdil_uncor_38X.png} |
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\caption{\label{fig:abcdMC}\protect Distributions of MET$/\sqrt{\rm SumJetPt}$ vs. |
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SumJetPt for SM Monte Carlo. Here we also show our choice of ABCD regions.} |
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> |
SumJetPt for SM Monte Carlo. Here we also show our choice of ABCD regions. The correlation coefficient |
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${\rm corr_{XY}}$ is computed for events falling in the ABCD regions.} |
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\end{center} |
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\end{figure} |
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|
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in the four regions for the SM Monte Carlo, as well as the background |
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prediction A $\times$ C / B are given in Table~\ref{tab:abcdMC} for an integrated |
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luminosity of 35 pb$^{-1}$. The ABCD method with chosen boundaries is accurate |
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to about 20\%, and we assess a corresponding systematic uncertainty. |
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> |
to about 20\%, and we assess a corresponding systematic uncertainty |
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> |
{\bf \color{red} More detail needed here???} |
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|
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%As shown in Table~\ref{tab:abcdsyst}, we assess systematic uncertainties |
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%by varying the boundaries by an amount consistent with the hadronic energy scale uncertainty, |
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|
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\newcommand{\ptll} {\ensuremath{P_T(\ell\ell)}} |
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\begin{center} |
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$ K = \frac{\int_0^{\infty} {\cal N}(\ptll)~~d\ptll~}{\int_{50}^{\infty} {\cal N}(\ptll)~~d\ptll~}$ |
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> |
$ K = \frac{\int_0^{\infty} {\cal N}(\ptll)~~d\ptll~}{\int_{50}^{\infty} {\cal N}(\ptll)~~d\ptll~} = 1.52$ |
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|
\end{center} |
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|
152 |
|
|
151 |
– |
Monte Carlo studies give values of $K$ that are typically between 1.5 and 1.6, |
152 |
– |
depending on selection details. |
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|
%%%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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up & 0.92 $ \pm $ 0.11 & 1.53 $ \pm $ 0.12 & 1.66 $ \pm $ 0.23 \\ |
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down & 0.81 $ \pm $ 0.07 & 1.08 $ \pm $ 0.11 & 1.32 $ \pm $ 0.17 \\ |
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|
\hline |
237 |
– |
|
238 |
– |
\hline |
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|
MET smearing & Predicted & Observed & Obs/pred \\ |
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|
\hline |
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nominal & 0.92 $ \pm $ 0.11 & 1.29 $ \pm $ 0.11 & 1.40 $ \pm $ 0.20 \\ |
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40\% & 0.85 $ \pm $ 0.07 & 1.37 $ \pm $ 0.11 & 1.61 $ \pm $ 0.19 \\ |
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50\% & 1.08 $ \pm $ 0.18 & 1.36 $ \pm $ 0.11 & 1.26 $ \pm $ 0.24 \\ |
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|
\hline |
248 |
– |
|
249 |
– |
\hline |
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|
non-$t\bar{t} \to$~dilepton scale factor & Predicted & Observed & Obs/pred \\ |
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|
\hline |
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ttdil only & 0.77 $ \pm $ 0.07 & 1.05 $ \pm $ 0.06 & 1.36 $ \pm $ 0.14 \\ |
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We have studied this effect at the generator level using Alpgen. We find |
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that the bias is at the few percent level. |
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|
|
280 |
< |
Based on the results of Table~\ref{tab:victorybad}, we conclude that the |
280 |
> |
Based on the results of Table~\ref{tab:victorysyst}, we conclude that the |
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|
naive data-driven background estimate based on $P_T{(\ell\ell)}$ needs to |
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< |
be corrected by a factor of $ K_C = 1.4 \pm 0.2(stat)$. |
282 |
> |
be corrected by a factor of $ K_C = 1.4 \pm 0.2({\rm stat})$. |
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|
|
284 |
< |
The 2 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. The impact of non-$t\bar{t}$-dilepton background is assessed |
286 |
< |
by varying the yield of all backgrounds except for $t\bar{t} \to$~dilepton, as shown in Table~\ref{table_kc}. |
287 |
< |
The systematic is assessed as the larger of the differences between the nominal $K_C$ value and the values |
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 |
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|
obtained using only $t\bar{t} \to$~dilepton MC and obtained by doubling the non $t\bar{t} \to$~dilepton component, |
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giving an uncertainty of $0.04$. |
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|
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|
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{} and checking how much $K_C$ changes, as summarized in Table~\ref{tab:victorysyst}. |
294 |
< |
This gives an uncertainty of 0.3. We also assess the impact of the MET resolution uncertainty on $K_C$ by applying |
295 |
< |
a random smearing to the MET. For each event, we determine the expected MET resolution based on the sumJetPt, and |
296 |
< |
smear the MET to simulate an increase in the resolution of 10\%, 20\%, 30\%, 40\% and 50\%. The results show that |
300 |
< |
$K_C$ does not depend strongly on the MET resolution and we therefore do not assess any uncertainty. |
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 |
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|
298 |
|
Incorporating all the statistical and systematic uncertainties we find $K_C = 1.4 \pm 0.4$. |
299 |
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|