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% title slide definition
\title{Updates on mixing.}
%\subtitle{a bias report}
\author{ Marcin Chrz\k{a}szcz$^{1,2}$ ,  Nicola Serra$^{1}$ }
\institute[UTH, IFJ]
{
 %\begin{tiny}
$ ^1$ University of Zurich , $ ^2$ Institute of Nuclear Physics, Krakow,  
 %\end{tiny}smallsmall
}
  

\date{ \begin{small} $30^{th}$ September 2013 \end{small}}

%--------------------------------------------------------------------
%                           Introduction
%--------------------------------------------------------------------

\begin{document}




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%--------------------------------------------------------------------
%                          OUTLINE
%--------------------------------------------------------------------




\section[Outline]{}
\begin{frame}
\tableofcontents
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%-------------------------------------------------------------------
%                          Introduction
%-------------------------------------------------------------------
%
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\title{Update on analysis}


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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%2>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}\frametitle{}
For 2011 we did 2 times one dimensional binning optimisation. This method has disadvantages: 
\begin{itemize}
\item The best bin of one classifier is split to very small pieces by the other optimisation.
\item You end up having best bins in the middle. See Pauls presentation.
\end{itemize}

	\textref {M.Chrz\k{a}szcz, N.Serra 2013}

\end{frame}


\begin{frame}\frametitle{How to fix?}
\only<1>
{
\begin{itemize}
\item Perform a simultaneous optimisation in 2D.
\item Brutal force method is not good enough, cuz number of combination explodes.
\item Use MC methods for optimisation.
\item Takes 3 hours to optimise.
\item Use Punzi FOM instead of Cls method.
\item Use 2011 data to optimise.
\item Apply Fine-tuning. You need a given number of events to perform a stable fit etc.
\end{itemize}
}
\only<2>
{
  \includegraphics[scale=0.23]{inflaton/punzi1.png}

}
	\textref {M.Chrz\k{a}szcz, N.Serra 2013}

\end{frame}








\section{Isolation}
\subsection{Isolation optimisation}

\begin{frame}\frametitle{Iso optimisation}
{~}


\only<1>
{	
	Till now every analysis that used track isolation parameter used the ones develeloped and optimised for $B_s \to \mu \mu$.
	This is based on an abstract definitions of isolating and non-isolating tracks:
	\begin{itemize}
	\item Non-isolating track to a given track($\mu$ from $B_s \to \mu \mu$ for example) will be a track that has the same primary mother as muon. 
	\item Isolating is the negation of non-isolating. 
\end{itemize}	
{~} \\
	\begin{Large}
	Many thanks to Giampi for discussion and advises !
	\end{Large}
	
}

\only<2>
{	
	This definition has potentially dangerous implications.
	  	\begin{itemize}
	  	\item Imagine a long chain of decays. Every of this decay is non-isolating.
	  	\item Why very long living particles ($\Lambda$, $K_s$) have to be considered non-isolating?
	  	\item When we do our analysis we are operating on basis of signal and bck hypothesis.
	  	\item There isnt a 1:1 correspondence between isolating and bck etc.
		\item $B_s \to \mu \mu$ does not really suffer from this. 	
	
	  	
	  		  		\end{itemize}	
	  	


}

	\textref {M.Chrz\k{a}szcz, N.Serra 2013}

\end{frame}



\begin{frame}\frametitle{How to train?}
{~}



\only<1>
{	
	\begin{enumerate}
	
	
	\item The main point of isolation variable is to fight again combinatorial bck.(example two decays trees are close and one picks something from the other).
	\item We build our bck sample taking from MC truth the candidates that are combinatorial bck. 
		
	\end{enumerate}
		
}
\only<2>
{	
	
	Now I will loose you all :P
	\begin{enumerate}
	
	
	\item We need to swap our signal and bck sample.
	\item Why? Our signal sample contains: signal candidate(3 tracks)+ tracks surrounding this candidate. Our selection should be optimised in a way that we should end up with our single signal candidate without any tracks nearby.
	\item That is why our signal sample is our background sample.
				
		
	\end{enumerate}
		
}
\only<3>
{	
	\begin{enumerate}

	\item We define the training variables as Giampi did:+tckchi2+IP.
	\item We put everything inside tmva.
	\item Then we scan the BDT response space and write how many tracks survive the cut.
	\item Optimisation of the cut has to be done inside the BDT that we will use.
	
	
		
	\end{enumerate}
		
}
\only<4>
{	
	\begin{enumerate}

	\item In practice what we do is to scan BDT form 0. to 0.5 and count the tracks for each of the BDT value.
	\item Then our new ntuple will have like 100 isolation parameters.
	\item How to choose the best one?
	\item Well isolation parameter on its own is useless. It has to be combined with other variables in TMVA. Than you can choose the best cut on the BDT.
	
	
		
	\end{enumerate}
		
}

	\textref {M.Chrz\k{a}szcz, N.Serra 2013}

\end{frame}

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\begin{frame}\frametitle{$\tau \to 3 \mu$ specifics}
{~}


\only<1>
{	
In case of $\tau \to 3 \mu$ we want different isolation parameters for different kinds of decays:
	\begin{itemize}
	\item $D \to \tau$
	\item $Ds \to \tau$		
	\item $B \to D \to \tau$
	\item $B \to Ds \to \tau$			
	\item $B \to \tau$	
	\end{itemize}
	
	
}
\only<2>
{	
\begin{enumerate}

\item Does it make any sense to make my life so complicated?
\item YES! 
\item Example: $B \to \tau$ is in $99 \%$ $B \to D \tau X$.
\item This means we if you have D and tau close to each other track from D can go to $\tau$ etc. 
\item In their approach this truck would be considered non-isolating which is nonsense because it forms a bck candidate!
\item From first looks the problem can be reduced to 3 chains:  $B \to \tau$, 
 $B \to Dx \to \tau$, $D \to \tau$.
\end{enumerate}
	
	
}
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  \includegraphics[scale=0.21]{tmva_tau23mu/mva_BDT.png}


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 \includegraphics[scale=0.21]{tmva_tau23mu/rejBvsS_21513000.png}
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}







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\end{document}
