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% Seth Brown, Ph.D.
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% see the macros.tex file for definitions
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% title slide definition
\title{Updates on activities.}
%\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} $16^{th}$ July 2013 \end{small}}

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

\begin{document}




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




\section[Outline]{}
\begin{frame}
\tableofcontents
\end{frame}







%-------------------------------------------------------------------
%                          Introduction
%-------------------------------------------------------------------
%
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\title{Update on analysis}


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\section{Inflaton analysis}
\subsection{MC samples}
\begin{frame}\frametitle{MC Samples}
\only<1>
{
To Study the behaviour of signal I am producing MC samples with different Inflaton life time:
\begin{enumerate}
\item $1 \times 10^{-10} s$, 1M
\item $2.5 \times 10^{-10} s$, 1M
\item $5 \times 10^{-10} s$, 1M
\item $7.5 \times 10^{-10} s$, 1.5M
\item $10 \times 10^{-10} s$, 1.5M
\end{enumerate}

}
\only<2>{
To Study the behaviour of signal I am producing MC samples with different Inflaton life time:
\begin{enumerate}
\item \textcolor{green}{$1 \times 10^{-10}$ 1M}
\item \textcolor{green}{$2.5 \times 10^{-10}$ 1M}
\item  \textcolor{green}{$5 \times 10^{-10}$ 1M}
\item  \textcolor{yellow}{$7.5 \times 10^{-10}$ 1.5M}
\item  \textcolor{red} {$10 \times 10^{-10}$ 1.5M}
\end{enumerate}
Sorry not everything green  \Simley{-1} \Simley{-1} \Simley{-1}
}


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

%\section{Work done so far}



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%55
\subsection{Normalization Channel}
\begin{frame}

\frametitle{Normalization channel}
{~}
For the first idea we wanted to use $B^0 \to J/\psi K_s$.
I had to compare how $K_s$ imitates our signal inflaton on MC.:

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 \includegraphics[scale=0.23]{pic2/KS_IPCHi2_down.png}


\end{columns}

	
		

	
}
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	IP
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	DownStream\\
 \includegraphics[scale=0.23]{pic2/KS_IP_down.png}


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}

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	Cone isolation
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		\textref {M.Chrz\k{a}szcz, N.Serra 2013}
\end{frame}




\begin{frame}\frametitle{Summary on inflaton}

\begin{enumerate}
\item Bid difference between control channel and signal \Simley{-1}
\item Different control channel? Some $\Lambda$ channel?
\item Reweigh MC?
\end{enumerate}

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






\section{$\tau \to 3\mu$}
\subtitle{MC troubles}
\begin{frame}
\only<1>{
Making a cross check I found out that our geometry is surprisingly higher than last year:
\begin{enumerate}
\item MC11: $17.28\%$
\item Sim08a: $19.14\%$
\end{enumerate}
Didn't believe that $2\%$ is due to the higher CM energy. 
}
\only<2>
{
I run Generator Level MC to studied what has changed. Turns out this is pythia fault:
\begin{enumerate}
\item MC11, pythia6: $17.28\%$
\item Sim08a, pythia6: $17.96\%$
\item Sim08a, pythia6: $19.14\%$
\end{enumerate}
To make story more funny this is true for all MC samples. With Jon Harrison we checked yesterday: $B_s\to \mu \mu$, $D_s \to \eta \mu \nu $ etc.
We will let Gloria know about this. Does this change something in $B_s\to \mu \mu$?





}


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



\subsection{Peaking background}
\begin{frame}
\frametitle{Peaking background}

For $\tau \to 3 \mu$ we have irreducible background: $D_s \to \eta(\mu \mu \gamma) \mu \nu$.\\ I performed fit to all our bins:

\includegraphics[scale=0.3]{pic2/2d-data.pdf}

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


\begin{frame}
\frametitle{Peaking background}


	\begin{columns}
\column{2.5in}
\includegraphics[scale=0.15]{pic2/pid_0p1_0p48geo0p74_1p1.png}


\column{2.5in}
\includegraphics[scale=0.15]{pic2/pid_-0p1_0p48geo0p35_0p65.png}


\end{columns}
All fits:(user:lhcb, password: 2924)

\url{http://nz17-p1.ifj.edu.pl/work_public/LHCb/Tau23Mu/Eta_fits/}


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



\begin{frame}
\subsection{TMVA Training}
\frametitle{TMVA Training}
\only<1>{
Our $\tau$s come from 5 difference sources:
\begin{enumerate}
\item $B \to \tau$
\item $B \to D^+ \to \tau$
\item $D^+ \to \tau$
\item $B \to D_s \to \tau$
\item $D_s \to \tau$
\end{enumerate}
}
\only<2>
{
	\begin{columns}
\column{3.5in}
Kaggle (leading machine learning competition platform).

\column{2.5in}
\includegraphics[scale=0.4]{pic2/kaggle.png}
\end{columns}
{~}\\
If you notice how people win this competition; you'll notice that sometimes people combine two or more algorithm into ensemble and get better results. \\
This is called blending.

Isn't $\tau \to 3 \mu$ perfect environment to play?
}



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



\begin{frame}
\frametitle{TMVA Training}
\only<1>{
Let's start with old BDT:




  \includegraphics[scale=0.4]{pic2/rejBvsS_oryginal.png}
}

\only<2>{
End lets compare on training on individual signal types:
$D_s \to \tau$

	\begin{columns}
\column{2.5in}

  \includegraphics[scale=0.26]{pic2/rejBvsS_oryginal.png}

\column{2.5in}
\includegraphics[scale=0.26]{pic2/rejBvsS_Ds.png}


\end{columns}

}
\only<3>{
End lets compare on training on individual signal types:
$B \to \tau$

	\begin{columns}
\column{2.5in}

  \includegraphics[scale=0.26]{pic2/rejBvsS_oryginal.png}

\column{2.5in}
\includegraphics[scale=0.26]{pic2/rejBvsS_B.png}


\end{columns}

}


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


\begin{frame}
\frametitle{Conclusions}
{~}
\begin{itemize}
\item Ds is very promising!
\item Our variables in BDT clearly not good for B channels.
\item Studies are ongoing. There are thousands of combinations of different MVA i want to try combine.




\end{itemize}

\end{frame}
\end{document}