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- % see the macros.tex file for definitions
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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
- }
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-
- \date{ \begin{small} $30^{th}$ September 2013 \end{small}}
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- %--------------------------------------------------------------------
- % Introduction
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- \begin{document}
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- %--------------------------------------------------------------------
- % OUTLINE
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- \section[Outline]{}
- \begin{frame}
- \tableofcontents
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- %-------------------------------------------------------------------
- % Introduction
- %-------------------------------------------------------------------
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- \title{Update on analysis}
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- \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}
-
- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5555
- \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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