| 5 |
|
Backgrounds from SM \Z production were estimated using the data-driven |
| 6 |
|
MET templates method, and backgrounds from $t\bar{t}$ were estimated using |
| 7 |
|
the data-driven opposite-flavor subtraction technique. We found no evidence |
| 8 |
< |
for anomalous yield beyond SM expectations and placed Bayesian 95\% CL upper limits |
| 8 |
> |
for anomalous yield beyond SM expectations and placed \statistics\ 95\% CL upper limits |
| 9 |
|
on the non SM yields in the loose (MET$>$\signalmetl~GeV) and tight signal regions (MET$>$\signalmett~GeV) |
| 10 |
|
of \ulloose~and \ultight~events, respectively. |
| 11 |
< |
We also quoted expected yields for the |
| 12 |
< |
%We also quoted upper limits on the quantity |
| 13 |
< |
%$\sigma \times BF \times A$, assuming efficiencies and uncertainties from the |
| 11 |
> |
%We also quoted expected yields for the |
| 12 |
> |
We also quote upper limits on the quantity |
| 13 |
> |
%$\sigma \times BF \times A$, |
| 14 |
> |
\sta\ for the |
| 15 |
> |
%assuming efficiencies and uncertainties from the |
| 16 |
|
benchmark SUSY processes LM4 and LM8 |
| 17 |
< |
including estimated signal efficiencies including systematics. |
| 17 |
> |
including estimated signal efficiencies and systematics, |
| 18 |
> |
and conclude that LM4 is not compatible with the data and therefore ruled out. |
