| 218 |
|
\hline |
| 219 |
|
{\bf Quantity} & {\bf Requirement}\\ |
| 220 |
|
\hline |
| 221 |
< |
$|dz|$ & $< 0.1\rm~cm$ \\ |
| 221 |
> |
$|dz|$ & $< 0.1\rm~cm$ \\ |
| 222 |
|
$H/E$ & $< 0.12(0.1) EB(EE)$ \\ |
| 223 |
|
$iso_{trk}$ & $<0.3$ \\ |
| 224 |
|
$iso_{em}$ & $<0.3$ \\ |
| 230 |
|
\end{table} |
| 231 |
|
%------------------------------------------------- |
| 232 |
|
|
| 233 |
< |
MV discrimination is performed using the following variables : $\sigma_{i\eta i\eta}$, $\sigma_{i\phi i\phi}$, $\Delta\eta_{in}$, $\Delta\phi_{in}$, $f_{Brem}$, $n_{Brem}$, $E/P$, $d_{0}$, $E_{seed}/P_{out}$, $E_{seed}/P_{in}$, $1/E - 1/P$. {\bf Cuts on these guys? Show correlation plot to motivate BDT?} |
| 233 |
> |
MV discrimination is performed using the following variables : $\sigma_{i\eta i\eta}$, $\sigma_{i\phi i\phi}$, $\Delta\eta_{in}$, $\Delta\phi_{in}$, $f_{Brem}$, $n_{Brem}$, $E/P$, $d_{0}$, $E_{seed}/P_{out}$, $E_{seed}/P_{in}$, $1/E - 1/P$. As can be seen in Figure~\ref{fig:bdtInput}, these variables exhibit substantial correlations, of which the BDT is able to make full use. The same figure also displays the input distributions for signal and background for several representative variables. |
| 234 |
> |
|
| 235 |
> |
%------------------------------------------------- |
| 236 |
> |
\begin{figure}[tbp] |
| 237 |
> |
\begin{center} |
| 238 |
> |
\includegraphics[width=0.4\linewidth]{figs/bdt-correl-sig.png} |
| 239 |
> |
\includegraphics[width=0.4\linewidth]{figs/bdt-correl-bkg.png} |
| 240 |
> |
\includegraphics[width=0.4\linewidth]{figs/bdt-input-OneOverEMinusOneOverP.png} |
| 241 |
> |
\includegraphics[width=0.4\linewidth]{figs/bdt-input-DEtaIn.png} |
| 242 |
> |
\caption{ \label{fig:bdtInput} } |
| 243 |
> |
\end{center} |
| 244 |
> |
\end{figure} |
| 245 |
> |
|
| 246 |
> |
{\bf Cuts on these guys? Show correlation plot to motivate BDT?} |
| 247 |
|
|
| 248 |
|
We train and validate the BDT using statistically independent subsets of events from the samples described above. Training and testing is performed separately for six $\eta/p_{T}$ bins. A cut on the resulting BDT discriminant translates to a specific combination of signal and background efficiency. The locus of signal/background efficiencies yields the performance ({\it i.e:} ROC) curves shown in Figure~\ref{fig:ROC}. |
| 249 |
|
|
