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initial analysis is simply a counting experiment in the tail of the $M_T$ distribution. |
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|
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The event selection is one-and-only-one high \pt\ isolated lepton, four or more jets, and |
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an \met\ cut. At least one of the jets has to be btagged to reduce $W+$ jets. |
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an \met\ cut. At least one of the jets has to be b tagged to reduce $W+$ jets. |
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The event sample is then dominated by $t\bar{t}$, but there are also contributions from $W+$ jets, |
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single top, dibosons, as well as rare SM processes such as $ttW$. |
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|
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% tail has to be controlled at the level of 10\% or better. So this is (almost) a precision measurement. |
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|
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The $t\bar{t}$ events in the $M_T$ tail can be broken up into two categories: |
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(i) $t\bar{t} \to \ell $+ jets and (ii) $t\bar{t} \to \ell^+ \ell^-$ where one of the two |
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(i) $t\bar{t} \to \ell $+ jets, and (ii) $t\bar{t} \to \ell^+ \ell^-$ where one of the two |
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leptons is not found by the second-lepton-veto (here the second lepton can be a hadronically |
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decaying $\tau$). |
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For a reasonable $M_T$ cut, say $M_T >$ 150 GeV, the dilepton background is approximately 80\% of |
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For a reasonable $M_T$ cut, say $M_T >$ 150 GeV, the dilepton background is approximately 70\% of |
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the total. This is because in dileptons there are two neutrinos from $W$ decay, thus $M_T$ |
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is not bounded by $M_W$. This is a very important point: while it is true that we are looking in |
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the tail of $M_T$, the bulk of the background events end up there not because of some exotic |
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% Sophisticated fully ``data driven'' techniques are not really needed. |
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|
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One general point is that in order to minimize systematic uncertainties, the MC background |
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predictions are whenever possible normalized to the bulk of the $t\bar{t}$ data, ie, events passing all of the |
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predictions are whenever possible normalized to the bulk of the $t\bar{t}$ data, i.e., events passing all of the |
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requirements but with $M_T \approx 80$ GeV. |
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This (mostly) removes uncertainties |
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due to $\sigma(t\bar{t})$, lepton ID, trigger efficiency, luminosity, etc. |
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|
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|
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For $W +$ jets the ability of the Monte Carlo to model this ratio |
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($R_{wjet}$) is tested in a sample of $\ell +$ jets enriched in |
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> |
($R_{wjet}$) is validated in a sample of $\ell +$ jets enriched in |
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$W +$ jets by the application of a b-veto. |
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The equivalent ratio for top events ($R_{top}$) is validated in a sample of well |
79 |
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The equivalent ratio for top events ($R_{top}$) is tested in a sample of well |
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identified $Z \to \ell \ell$ with one lepton added to the \met\ |
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calculation. |
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This sample is well suited to testing the resolution effects on |
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$t\bar{t}$/single top and $W +$ jets introduces a systematic |
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uncertainty in the background calculation because one needs |
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to know the relative fractions of these two components in |
90 |
< |
$M_T \approx 80$ GeV lepton $+$ jets sample. |
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> |
the $M_T \approx 80$ GeV lepton $+$ jets sample. |
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|
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|
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\subsection{Dilepton background} |
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\label{sec:dil-general} |
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|
96 |
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To suppress dilepton backgrounds, we veto events with an isolated track of \pt $>$ 10 GeV (see Sec.~\ref{sec:tkveto} for details). |
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To suppress dilepton backgrounds, we veto events with an isolated track of \pt\ $>$ 10 GeV (see Sec.~\ref{sec:tkveto} for details). |
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Being the common feature for electron, muon, and one-prong |
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tau decays, this veto is highly efficient for rejecting |
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$t\bar{t}$ to dilepton events. The remaining dilepton background can be classified into the following categories: |
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%\item 1-prong hadronic tau decay |
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%\item $e$ or $\mu$ possibly from $\tau$ decay |
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%\end{itemize} |
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%We have currently no veto against 3-prong taus. For the other two categories, we explicitely |
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%We have currently no veto against 3-prong taus. For the other two categories, we explicitly |
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%veto events %with additional electrons and muons above 10 GeV , and we veto events |
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%with an isolated track of \pt\ $>$ 10 GeV. This rejects electrons and muons (either from $W\to e/\mu$ or |
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%$W\to \tau\to e/\mu$) and 1-prong tau decays. |
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%Monte Carlo studies indicate that these three components populate the $M_T$ tail in the proportions of roughly 6\%, 47\%, 47\%. |
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We note that at present we do not attempt to veto 3-prong tau decays as they are about 15\% of the total dilepton background according to the MC. |
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|
129 |
< |
The high $M_T$ dilepton backgrounds come from MC, but their rate is normalized to the |
129 |
> |
The high $M_T$ dilepton background predictions come from MC, but their rate is normalized to the |
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$M_T \approx 80$ GeV peak. In order to perform this normalization in |
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data, the rare background events in the $M_T$ peak are subtracted off. This also introduces a systematic uncertainty. |
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|
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There are two types of effects that can influence the MC dilepton prediction: physics effects |
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and instrumental effects. We discuss these next, starting from physics. |
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|
136 |
< |
First of all, many of our $t\bar{t}$ MC samples (eg: MadGraph) have |
136 |
> |
First of all, many of our $t\bar{t}$ MC samples (e.g. MadGraph) have |
137 |
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BR$(W \to \ell \nu)=\frac{1}{9} = 0.1111$. |
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PDG says BR$(W \to \ell \nu) = 0.1080 \pm 0.0009$. This difference matters, so the $t\bar{t}$ MC |
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must be corrected to account for this. |
154 |
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uncertainty associated with the $t\bar{t}$ generator modeling. |
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|
156 |
|
The main instrumental effect is associated with the efficiency of the isolated track veto. |
157 |
< |
We use tag-and-probe to compare the isolated track veto performance in $Z + 4$ jet data and |
157 |
> |
We use tag-and-probe to compare the isolated track veto performance in $Z + 4$ jets data and |
158 |
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MC. Note that the performance of the isolated track veto |
159 |
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is not exactly the same on $e/\mu$ and on one prong hadronic tau decays. This is because |
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the pions from one-prong taus are often accompanied by $\pi^0$'s that can then result in extra |
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|
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\subsection{Other backgrounds} |
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\label{sec:other-general} |
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Other backgrounds are $tW$, $ttV$, dibosons, tribosons, Drell Yan. |
173 |
> |
Other backgrounds are $tW$, $ttV$, dibosons, tribosons, and Drell Yan. |
174 |
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These are small. They are taken from MC with appropriate scale |
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factors for trigger efficiency, and reweighting to match the distribution of reconstructed primary vertices in data. |
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|