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% see the macros.tex file for definitions
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% title slide definition
\title{MC, $\eta$, TMVA }
%\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} $25^{th}$ July 2013 \end{small}}
%--------------------------------------------------------------------
% Introduction
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\begin{document}
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%--------------------------------------------------------------------
% OUTLINE
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\section[Outline]{}
\begin{frame}
\tableofcontents
\end{frame}
%-------------------------------------------------------------------
% Introduction
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\title{Update on analysis}
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\section{MC studies}
\begin{frame}\frametitle{MC Signal}
{~}\\
Reminder:
\begin{itemize}
\item In 2011 we simulated a mixture of $\tau \to 3 \mu$.
\item We found out that the cross section is wrong in MC.
\item We reweighed all this distributions to match the correct cross section.
\item But what with DPC? This can't be reweighed!
\item Let's check how $\epsilon_{DPC}$ depends on signal channel.
\end{itemize}
\textref {M.Chrz\k{a}szcz, N.Serra 2013}
\end{frame}
%\section{Work done so far}
\begin{frame}\frametitle{Cross check procedure}
{~}\\
\only<1>{
Let's run Pythia6 with 8 TeV CM energy. With old decfile(aka the wrong mixture of $c\bar{c}$ and $b\bar{b}$. We get:
\begin{itemize}
\item $\epsilon_{DPC} =17.9 \%$
\item For $7 TeV\% $ we had:$17.7\%$
\item This part looks reasonable. We would expected a small gain.
\end{itemize}
}
\only<2>{
{~}\\
We then simulate two samples for each of 5 sources of $\tau$.
\begin{itemize}
\item 1st Sample with Geometry+Daughter\footnote{Daugher cuts forces $\tau$ to come from a specific mother. Ex. B.} Cuts. $\epsilon_{DPC+DAU}$
\item 2nd Sample with Daughter Cut. $\epsilon_{DAU}$
\end{itemize}
}
\textref {M.Chrz\k{a}szcz, N.Serra 2013}
\end{frame}
\begin{frame}\frametitle{MC Signal}
{~}\\
\begin{center}
\begin{tabular}{| l | l | l || l |}
\hline
$\tau$ source & $\epsilon_{DPC+DAU} [\%]$ & $\epsilon_{DAU} [\%]$ & $\epsilon_{DPC} [\%]$ \\ \hline \hline
$D \to \tau$ & $12.12 \pm 0.07$ & $32.71 \pm 0.13$ & $ 18.5 \pm 0.1$ \\ \hline
$B \to D \to \tau$ & $1.36 \pm 0.01$ & $3.99 \pm 0.03$ & $ 17.0 \pm 0.1$ \\ \hline
$D_s \to \tau$ & $11.79 \pm 0.07$ & $31.53 \pm 0.13$ & $ 18.6 \pm 0.1$ \\ \hline
$B \to D_s \to \tau$ & $1.75 \pm 0.01$ & $5.04 \pm 0.03$ & $ 17.4 \pm 0.1$ \\ \hline
$B \to \tau$ & $5.16 \pm 0.05$ & $14.85 \pm 0.13$ & $ 17.4 \pm 0.2$ \\ \hline \hline
\end{tabular}
\end{center}
\only<1>
{
Let's take wrong weights from MC and calculate the $\epsilon_{DPC}$:\\
\textcolor{green}{
$\epsilon_{DPC, WRONG}=17.86$} , with agriment with simulating the wrong mixture from beginning!
}
\only<2>
{
Let's take wrong weights from MC and calculate the $\epsilon_{DPC}$:\\
\textcolor{green}{
$\epsilon_{DPC, WRONG}=17.86 \%$} , with agriment with simulating the wrong mixture from beginning!
If we take the correct weights we obtain:\\
\textcolor{red}{
$\epsilon_{DPC, CORRECT}=18.60 \%$. We underestimated our efficiency!
}
}
\only<3>
{
How ever the overall effect will be smaller cuz the same thing will happen for the normalization channel.
}
\textref {M.Chrz\k{a}szcz, N.Serra 2013}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}\frametitle{Pythia Wars}
{~}\\
I have found an other disturbing thing. Lets compare pythia 6 with pythia8:
\begin{center}
\begin{tabular}{| l | l | }
\hline
{~} & $\epsilon_{DPC} [\%]$ \\ \hline \hline
Pythia 6 & $ 17.9$ \\ \hline
Pythia 8 & $ 19.1$ \\ \hline \hline
\end{tabular}
\end{center}
This looks worse than it is. Jon checked and this happens not only to $\tau \to 3 \mu$. Turn out this is common. $B_s \to \mu \mu$ aslo has the same problem. However thanks to normalization this the ratio of efficiencies changes by $0.1\%$.\\
We are safe anyway.
\textref {M.Chrz\k{a}szcz, N.Serra 2013}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%55
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
\section{$\eta$ fits}
\begin{frame}\frametitle{$\eta$ fits}
%Do the Charm trigger lines really hurt us?
\only<1>{
\begin{itemize}
\item Till yesterday we took $\eta$ for fitting directly from MC.
\item But how much eta is there?
\item We might have combinatorial background with partially reconstructed $\eta$.
\item Lots of thanks to Paul for speedy implementation of this idea!
\item To increase the sensitivity I took left mass range larger! Make the fit more stable.
\end{itemize}
}
\only<2>{
Extreme case: Trash bins
{~}\\
\begin{columns}
\column{2.5in}
Only $\eta$ \\
\includegraphics[scale=0.165]{fits/new/pid_-0p1_0p48geo-1p1_-0p48.png}
\column{2.5in}
$\eta$ with combinatorics.\\
\includegraphics[scale=0.165]{fits/old/pid_-0p1_0p48geo-1p1_-0p48.png}
\end{columns}
} %pid_0p6_0p65geo0p65_0p74.png
\only<3>{
Not only the trash bin is affected: pid $0.725 - 0.86$ \\
geo: $-0.48 - 0.05$
{~}\\
\begin{columns}
\column{2.5in}
Only $\eta$ \\
\includegraphics[scale=0.165]{fits/new/pid_0p725_0p86geo-0p48_0p05.png}
\column{2.5in}
$\eta$ with combinatorics.\\
\includegraphics[scale=0.165]{fits/old/pid_0p725_0p86geo-0p48_0p05.png}
\end{columns}
}
\only<4>{
As old Chinese wisdom says: "One event can make a difference"\\
Not only the trash bin is affected: pid $0.6 - 0.65$ \\
geo: $0.65 - 0.74$
{~}\\
\begin{columns}
\column{2.5in}
Only $\eta$ \\
\includegraphics[scale=0.165]{fits/new/pid_0p6_0p65geo0p65_0p74.png}
\column{2.5in}
$\eta$ with combinatorics.\\
\includegraphics[scale=0.165]{fits/old/pid_0p6_0p65geo0p65_0p74.png}
\end{columns}
}
\textref {M.Chrz\k{a}szcz, N.Serra 2013}
\end{frame}
\begin{frame}\frametitle{Conclusions on $\eta$}
{~}\\
\begin{itemize}
\item $23\%$ of events in the ntuple are background.
\item Much better shape of $\eta$.
\item PDF similar in each bin!
\item Much smaller linkage of $\eta$ to mass window!
\item PDFs are ready for fitting with 2012 data!
\end{itemize}
\textref {M.Chrz\k{a}szcz, N.Serra 2013}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
\section{TMVA}
\begin{frame}\frametitle{Introduction}
\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{First attempts}
{~}\\
\begin{itemize}
\item Let's take our background produced so far.
\item Already a comparable sample to 2011! Generator cuts are doing their job.
\item Let's train each signal on separate source of $\tau$.
\end{itemize}
\textref {M.Chrz\k{a}szcz, N.Serra 2013}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}\frametitle{$B \to \tau$}
{~}\\
We really suck in selecting this channel.
\includegraphics[scale=0.4]{tmva/ROC_31113002.png}
\textref {M.Chrz\k{a}szcz, N.Serra 2013}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
\begin{frame}\frametitle{$B \to D_s \to \tau$}
{~}\\
On the biggest contributing channel we are quite optimal.
\includegraphics[scale=0.4]{tmva/ROC_23513000.png}
\textref {M.Chrz\k{a}szcz, N.Serra 2013}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
\begin{frame}\frametitle{$D_s \to \tau$}
{~}\\
On the biggest contributing channel we are quite optimal.
\includegraphics[scale=0.4]{tmva/ROC_23513001.png}
\textref {M.Chrz\k{a}szcz, N.Serra 2013}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
\begin{frame}\frametitle{$B \to D^+ \to \tau$}
{~}\\
On the biggest contributing channel we are quite optimal.
\includegraphics[scale=0.4]{tmva/21513000_roc2.png}
\textref {M.Chrz\k{a}szcz, N.Serra 2013}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
\begin{frame}\frametitle{$D^+ \to \tau$}
{~}\\
On the biggest contributing channel we are quite optimal.
\includegraphics[scale=0.4]{tmva/ROC_21513001.png}
\textref {M.Chrz\k{a}szcz, N.Serra 2013}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
\begin{frame}\frametitle{Comparison on mix sample}
{~}\\
On the biggest contributing channel we are quite optimal.
\includegraphics[scale=0.4]{tmva/mix.png}
\textref {M.Chrz\k{a}szcz, N.Serra 2013}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
\begin{frame}\frametitle{Conclusions on TMVA}
{~}\\
\begin{itemize}
\item Each of the signal components is enormously larger than MVA trained on mix.
\item Method looks very promising if we can find a nice blending method(work for next week).
\item Mayby discusion on TMVA/MatrixNet/Neurobayes is next to leading order effect compared to this method?
\end{itemize}
\textref {M.Chrz\k{a}szcz, N.Serra 2013}
\end{frame}
\section{Plans for next week}
\begin{frame}\frametitle{Conclusions on TMVA}
{~}\\
\begin{itemize}
\item Finish producing cc bck
\item Continue blending.
\item Finish calculating new 2D binning optimisation(last night it was still calculating).
\item Start Normalizing the $\eta$
\item Produce Normalization channel MC.
\end{itemize}
\textref {M.Chrz\k{a}szcz, N.Serra 2013}
\end{frame}
\end{document}
Marcin Chrzaszcz