| 15 |
|
(tight signal region), and two or more jets. We use data driven techniques to predict the |
| 16 |
|
standard model background in these search regions. |
| 17 |
|
Contributions from Drell-Yan production combined with detector mis-measurements that |
| 18 |
< |
produce fake MET are modeled via MET templates based on photon plus jets events. |
| 18 |
> |
produce fake MET are modeled via MET templates based on photon plus jets or QCD events. |
| 19 |
|
Top pair production backgrounds, as well as other backgrounds for which the lepton |
| 20 |
|
flavors are uncorrelated such as WW and DY$\rightarrow\tau\tau$, are |
| 21 |
|
modeled via $e^\pm\mu^\mp$ subtraction. |
| 22 |
|
|
| 23 |
< |
As leptonically decaying \Z bosons is a signature that has very little background, |
| 23 |
> |
As leptonically decaying \Z bosons are a signature that has very little background, |
| 24 |
|
they provide a clean final state in which to search for new physics. |
| 25 |
|
Because new physics is expected to be connected to the Standard Model Electroweak sector, |
| 26 |
|
it is likely that new particles will couple to W and Z bosons. |
| 32 |
|
understand the tail of the fake MET distribution in \Z plus jets events. |
| 33 |
|
|
| 34 |
|
The basic idea of the MET template method~\cite{ref:templates1}\cite{ref:templates2} is |
| 35 |
< |
to measure the MET distribution in a control sample which has no true MET and a similar |
| 36 |
< |
topology to the signal events. |
| 37 |
< |
In our case, we choose a photon sample with two or more jets as the control sample. |
| 38 |
< |
Both the control sample and signal sample consist of a well measured object (either a |
| 39 |
< |
photon or a leptonically decaying $Z$), which recoils against a system of hadronic jets. |
| 40 |
< |
In both cases, the instrumental MET is dominated by mismeasurements of the hadronic system. |
| 35 |
> |
to measure the MET distribution in data in a control sample which has no true MET |
| 36 |
> |
and a similar topology to the signal events. |
| 37 |
> |
%Start the qcd vs photon discussion |
| 38 |
> |
Templates can be derived from either a QCD sample (as was done in the original implementation) |
| 39 |
> |
or a photon plus jets sample. |
| 40 |
> |
%In our case, we choose a photon sample with two or more jets as the control sample. |
| 41 |
> |
%Both the control sample and signal sample consist of a well measured object (either a |
| 42 |
> |
%photon or a leptonically decaying $Z$), which recoils against a system of hadronic jets. |
| 43 |
> |
In both cases, the instrumental MET is dominated by mismeasurements of the hadronic system, |
| 44 |
> |
and can be classified by the number of jets in the event and the scalar sum of their transverse |
| 45 |
> |
momenta. |
| 46 |
> |
The prediction is made such that the jet system in the control sample is similar to that of the |
| 47 |
> |
signal sample. |
| 48 |
> |
By using two independent control samples--QCD and photon plus jets--we are able to illustrate |
| 49 |
> |
the robustness of the MET templates method and to cross check the data driven background |
| 50 |
> |
prediction. |
| 51 |
|
|
| 52 |
|
This note is organized as follows. |
| 53 |
< |
In Sections~\ref{sec:datasets} and ~\ref{sec:trigSel} we start by describing |
| 54 |
< |
the triggers and datasets used, followed by the detailed object definitions (electrons, muons, photons, |
| 55 |
< |
jets, MET) and event selection which is described in Section~\ref{sec:eventSelection}. |
| 56 |
< |
We define a preselection and compare data vs. MC yields passing this preselection in Section~\ref{sec:yields}. |
| 57 |
< |
We then define the signal regions and show the number of observed events and MC expected yields in Section~\ref{sec:sigregion}. |
| 58 |
< |
Section~\ref{sec:templates} then introduces the MET template method and discusses its derivation |
| 59 |
< |
in some detail, followed by a demonstration in Section~\ref{sec:mc} that the method works in Monte Carlo. |
| 60 |
< |
Section~\ref{sec:topbkg} introduces the top background estimate based on opposite flavor subtraction, and contributions from other backgrounds are discussed in Section~\ref{sec:othBG}. |
| 53 |
> |
In sections \ref{sec:datasets} and \ref{sec:trigSel} we descibe |
| 54 |
> |
the datasets and triggers used, followed by the detailed object definitions (electrons, muons, photons, |
| 55 |
> |
jets, MET) and event selections in sections \ref{sec:evtsel} through \ref{sec:jetsel}. |
| 56 |
> |
We define a preselection and compare data vs. MC yields passing this preselection in |
| 57 |
> |
Section~\ref{sec:yields}. |
| 58 |
> |
We then define the signal regions and show the number of observed events and MC expected |
| 59 |
> |
yields in Section~\ref{sec:sigregion}. |
| 60 |
> |
Section~\ref{sec:templates} introduces the MET template method and discusses its derivation |
| 61 |
> |
in some detail and is followed by a demonstration in Section~\ref{sec:mc} |
| 62 |
> |
that the method works in Monte Carlo. |
| 63 |
> |
Section~\ref{sec:topbkg} introduces the top background estimate based on opposite flavor subtraction, |
| 64 |
> |
and contributions from other backgrounds are discussed in Section~\ref{sec:othBG}. |
| 65 |
|
Section~\ref{sec:results} shows the results for applying these methods in data. |
| 66 |
|
We analyze the systematic uncertainties in the background prediction in Section~\ref{sec:systematics} |
| 67 |
< |
and proceed to calculate an upper limit on the non SM contributions to our signal regions in Section~\ref{sec:upperlimit}. In Section~\ref{sec:models} we calculate upper limits on the quantity $\sigma \times BF \times A$, assuming |
| 68 |
< |
efficiencies and uncertainties from sample benchmark SUSY processes. We conclude in Section~\ref{sec:conclusion}. |
| 67 |
> |
and proceed to calculate an upper limit on the non-SM contributions to our signal regions |
| 68 |
> |
in Section~\ref{sec:upperlimit}. |
| 69 |
> |
Finally, in Section~\ref{sec:models} we calculate upper limits on the quantity |
| 70 |
> |
$\sigma \times BF \times A$, |
| 71 |
> |
assuming efficiencies and uncertainties from sample benchmark SUSY processes. |
| 72 |
> |
|
