deletions | additions       diff --git a/background.tex b/background.tex  index 156deed..22c7968 100644  --- a/background.tex  +++ b/background.tex 
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Of these 8 backgrounds, the processes with a * after them exhibit a 2-to-1 ratio of $e \mu$ to $\mu \mu$ events as the final state. Using this, the number of $\mu \mu$ events present in the data that come from these background processes can be estimated from the total number of $e \mu$ events present in data. However, due to experimental variations, we do not expect $\frac{N_{e \mu}}{N_{\mu \mu}} = 2$. For this reason, the actual number used is computed in the $tt$ dominated control region using the ratio of $e \mu$ events to $\mu \mu$ events in data, subtracting off the events from non 2:1 backgrounds. This ratio was found to be $R_{2:1} = 2.01 \pm 0.033$  For those processes which do not exhibit the 2-to-1 ratio, the expected number of events is computed based on estimates produced by the Monte Carlo samples discussed in section \ref{sect:eventSelection}. Since the Monte Carlo simulations cannot be assumed to be perfect, the estimates for the non 2-to-1 processes were scaled based on the ratio in the $dy$ dominated control region of non 2-to-1 processes in data to those in Monte Carlo, i.e. $SF = \frac{N_{data} - N_{2:1}}{N_{non-2:1}}$. Where N corresponds to the number of $\mu \mu$  events in the $dy$ dominated control region. This value was calculated for each mass point but exhibited only small deviations and as such a weighted average was used when predicting the final event count. The final expression for the total count is as follows  \begin{equation}  \mathrm{Total\ Pred} = \left( \mathrm{N}_\mathrm{data} \mathrm{Data}_\mathrm{e \mu}  \div R_{2:1} \right) + \left( SF \times \mathrm{N}_\mathrm{non-2:1} \right) \end{equation}  where $R_{2:1}$ and $SF$ are defined as above, $Data_{e \mu}$ refers to the number of $e \mu$ events in data,  and N $N_{non-2:1}$  refers to the number of non 2-to-1  $\mu \mu$ events. events in Monte Carlo.