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\author{ {\fontspec{Trebuchet MS}Marcin Chrz\k{a}szcz} (Universit\"{a}t Z\"{u}rich)}
\institute{UZH}
\title[$\PBzero \to \PK \Pmuon \APmuon$ update]{$\PBzero \to \PK \Pmuon \APmuon$ update}
\date{12 January 2016}
 
 
\begin{document}
\tikzstyle{every picture}+=[remember picture]
 
{
\setbeamertemplate{sidebar right}{\llap{\includegraphics[width=\paperwidth,height=\paperheight]{bubble2}}}
\begin{frame}[c]%{\phantom{title page}} 
\begin{center}
\begin{center}
	\begin{columns}
		\begin{column}{0.75\textwidth}
			\flushright\fontspec{Trebuchet MS}\bfseries \Huge {$\PBzero \to \PKstar \Pmuon \APmuon$ selection update}
		\end{column}
                \begin{column}{0.02\textwidth}
                  {~}
                  \end{column}
                \begin{column}{0.23\textwidth}
                 % \hspace*{-1.cm}
                  \vspace*{-3mm}
                  \includegraphics[width=0.6\textwidth]{lhcb-logo}
                  \end{column}
	        
	\end{columns}
\end{center}
	\quad
	\vspace{3em}
\begin{columns}
\begin{column}{0.44\textwidth}
\flushright \vspace{-1.8em} {\fontspec{Trebuchet MS} \Large Marcin Chrząszcz\\ Małgorzata Pikies\\ }
 
\end{column}
\begin{column}{0.53\textwidth}
\includegraphics[height=1.3cm]{uzh-transp}
\end{column}
\end{columns}
 
\vspace{1em}
%		\footnotesize\textcolor{gray}{With N. Serra, B. Storaci\\Thanks to the theory support from M. Shaposhnikov, D. Gorbunov}\normalsize\\
\vspace{0.5em}
 
	\textcolor{normal text.fg!50!Comment}{LUV meeting, CERN\\12 January 2016}
\end{center}
\end{frame}
}
 
 
\begin{frame}\frametitle{Selection requirements}
\begin{itemize}
\item Used good old Stripping line: \texttt{B2XMuMu\_Line} (S21).
\item In additional to stripping cuts $DLL_K>1$ applied.
\item Trigger used: 
\end{itemize} 
 \begin{tabular}{|c|}
 \hline
\texttt{L0DiMuonDecision}, \texttt{L0MuonDecision} \\
 \hline
 \texttt{Hlt1TrackMuonDecision},  \texttt{Hlt1TrackAllL0Decision}\\
 \hline
 \texttt{Hlt2Topo(2,3)BodyBBDTDecision},\\ \texttt{Hlt2TopoMu(2,3)BodyBBDTDecision}, \texttt{Hlt2DiMuonDecision}\\ \hline
  \end{tabular}
 \begin{itemize}
 \item All lines in TOS.
\item Selection follows previous $\PB \to \PK \Pmu \Pmu$ analysis.
 \end{itemize}
 
 
 
\end{frame}
 
 
 
\begin{frame}\frametitle{MVA training}
 
\begin{itemize}
\item Used the standard k-Folding technique with 10 folds.
\item As always randomized the folds etc.
\item As signal proxy used the \it{Splot} $\PBplus \to \PKplus \PJpsi$.
\item Mass modelled with double CB and single exponent.
\end{itemize}
\begin{center}
\includegraphics[width=0.45\textwidth]{images/Jpsi.png}
\end{center}
 \begin{itemize}
\item The fitted parameters in agreement with previous analysis.
\end{itemize}
 
\end{frame}
 
\begin{frame}\frametitle{BDT}
 
\begin{itemize}
\item Used the standard k-Folding technique with 10 folds.
\item Variables used in the training:
\end{itemize}
\only<1>{
\begin{center}
 
 \begin{tabular}{|c|}
 \hline
\texttt{K\_Kst\_IPCHI2\_OWNPV} \\
\texttt{B\_plus\_ENDVERTEX\_CHI2} \\
\texttt{e\_minus\_IPCHI2\_OWNPV} \\
\texttt{e\_plus\_IPCHI2\_OWNPV} \\
\texttt{J\_psi\_1S\_IPCHI2\_OWNPV}\\
\texttt{B\_plus\_PT}\\
\texttt{B\_plus\_IPCHI2\_OWNPV}\\
\texttt{B\_plus\_FD\_OWNPV}\\
\texttt{B\_plus\_DiraAngle}\\
\texttt{B\_plus\_P}\\
\texttt{K\_Kst\_P}\\
\hline
 
  \end{tabular}
 
\end{center}
}
\only<2>{
\begin{itemize}
\item Standard BDT training no optimisation:
\end{itemize}
 
\includegraphics[width=0.32\textwidth]{images/BDT_B_TEST.png}
\includegraphics[width=0.32\textwidth]{images/BDT_S_TEST.png}
\includegraphics[width=0.32\textwidth]{images/ROC_BDT.png}
\begin{itemize}
\item All folds are in very good agreement!
\end{itemize}
}
\only<3>{
\begin{itemize}
\item Standard BDT training optimisation (optimized on area under the ROC curve):
\end{itemize}
 
\includegraphics[width=0.32\textwidth]{images/BDT_B_TEST_OPT.png}
\includegraphics[width=0.32\textwidth]{images/BDT_S_TEST_OPT.png}
\includegraphics[width=0.32\textwidth]{images/ROC_BDT_OPT.png}
\begin{itemize}
\item 2 folds are different, but this is only the artefact of the BDT.
\end{itemize}
}
\only<4>{
\begin{itemize}
\item Standard BDT training optimisation (optimized on area under the ROC curve):
\end{itemize}
 
\includegraphics[width=0.32\textwidth]{images/BDT_B_TEST_OPTPID.png}
\includegraphics[width=0.32\textwidth]{images/BDT_S_TEST_OPTPID.png}
\includegraphics[width=0.32\textwidth]{images/ROC_BDT_OPTPID.png}
\begin{itemize}
\item Again the artefact of the optimisation.
\end{itemize}
}
 
 
\end{frame}
\begin{frame}\frametitle{Performance comparison}
 
\begin{itemize}
\item Now let's compare the gain on optimizing the parameters of the BDT.
\end{itemize}
\includegraphics[width=0.32\textwidth]{{images/ROC_BDT_0}.png}
\includegraphics[width=0.32\textwidth]{{images/ROC_BDT_1}.png}
\includegraphics[width=0.32\textwidth]{{images/ROC_BDT_2}.png}
 
\begin{itemize}
\item Clearly the PID and optimisation helps in the MVA!
\end{itemize}
\end{frame}
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
\begin{frame}\frametitle{To do}
 
\begin{itemize}
\item Fix the artefacts in of the DBT training for the optimised observables.
\item Days before Xmas Danny added the utility to EOS to generate events, so I plan to produce toy MC that can be used for studies and later on for systematics.
\item Start looking at the acceptance correction:
\begin{itemize}
\item Cross check that all variables are modeled well in MC(should be as they have been ok for $\PB \to \PKstar \mu \mu$).
\item From Kaggle contests there was an idea to use a BDT driven method to unfold the distributions. 
\item Will also cross check the normal moments method of unfolding.
\end{itemize}
 
 
\end{itemize}
 
\end{frame}
 
\backupbegin   
 
\begin{frame}\frametitle{Backup}
 
\end{frame}
 
\backupend			
 
\end{document}