\documentclass[xcolor=dvipsnames,table]{beamer}





    \author[Paul Seyfert]{
      Johannes Albrecht\inst{1}, Marta Calvi\inst{2}, Marcin Chrz\k{a}szcz\inst{3,4}, Laura Gavardi\inst{1}, Jon Harrison\inst{5},  Basem Khanji\inst{2}, George Lafferty\inst{5}, Tatiana Likhomanenko\inst{6}, Eduardo Rodrigues\inst{5}, Nicola Serra\inst{4}, \underline{Paul Seyfert\inst{7}}}

      \institute[Uni Heidelberg]{
        \inst{1}Dortmund,
        \inst{2}Milano Bicocca,
        \inst{3}Cracow, \inst{4}Zurich, \inst{5}Manchester, \inst{6}Yandex, \inst{7}Heidelberg University
      }

      \date{\today}
      \subject{}


      \AtBeginSection[]
      {
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          \tableofcontents[currentsection,currentsubsection]
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      }


      \begin{document}
      \begin{frame}
        \titlepage
      \end{frame}


      \begin{frame}
        \begin{enumerate}
          \item introduction\vspace{.5em}
          \item multivariate technique\vspace{.5em}
          \item choice of triggers\vspace{.5em}
          \item normalisation\vspace{.5em}
          \item backgrounds\vspace{.5em}
          \item expected sensitivity\vspace{.5em}
          \item model dependence\vspace{.5em}
        \end{enumerate}
        Major news wrt.\ the previous analysis rounds highlighted in \textcolor{darkgreen}{green}
      \end{frame}

      \section{introduction}

      \begin{frame}
        \frametitle{Status $\Ptau\to\Pmu\Pmu\Pmu$}
        \begin{columns}
          \begin{column}{.62\textwidth}
            \begin{fmffile}{sm}
              \begin{fmfgraph*}(200,150)
                \fmfstraight
                \fmfleft{is3,is2,is1,i1,i2,i3,i4,i5,i6}
                \fmfright{os3,os2,os1,o1,o2,o3,o4,o5,o6}


                \fmf{phantom}{o1,g1}
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                \fmffreeze

                \fmf{photon, tension=2.,lab.side=left, lab=$\Pphoton$}{g1,g2}
                \fmf{fermion,lab.side=left,label=$\APmuon$}{o3,g2}
                \fmf{fermion,lab.side=left,label=$\Pmuon$}{g2,o5}
                \fmf{phantom,tension=1.5}{i6,g2}

                \fmffreeze


                \fmf{fermion,lab.side=left,label=$\Pgtm$}{i1,w1}
                \fmf{dashes,lab.side=right,label=$\PWminus$}{w1,w2}
                \fmf{fermion,lab.side=left,label=$\Pmuon$}{w2,o1}


                \fmfipath{p[]}
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                \fmfiv{d.sh=cross,d.ang=0,d.size=5thick}{point length(p1)/2 of p1}


              \end{fmfgraph*}

            \end{fmffile}
            {\footnotesize{
              \begin{itemize}
                \item charged Lepton Flavour Violation process
                \item possible as penguin with neutrino oscillation
                \item unmeasurable small
              \end{itemize}
            }}
          \end{column}
          \begin{column}{.37\textwidth}
            \begin{block}{current limits ($ 90\,\%$ CL)}

              \begin{description}
                \item[BaBar] $3.3\times 10^{-8}$
                \item[Belle] $2.1\times 10^{-8}$
                \item[LHCb] $8.0\times 10^{-8}$
              \end{description}
            \end{block}
            \begin{block}{BSM predictions}
              \begin{description}
                \item[var.\ SUSY] $10^{-10}$
                \item[non universal $\PZprime$] $10^{-8}$
                \item[mSUGRA+seesaw] $10^{-9}$
              \end{description}
            \end{block}
          \end{column}
        \end{columns}
      \end{frame}

      \begin{frame}
        \frametitle{$\Ptau$ production}
        \begin{itemize}
          \item consider five production channels (fractions at $\unit{8}{\TeV}$):\begin{itemize}
                \item prompt $\PDs\to\Ptau$ ($73.1\pm3.9\,\%$)
                \item prompt $\PDplus\to\Ptau$ ($0.42\pm0.43\,\%$)
                \item non-prompt $\PDs\to\Ptau$ ($9.7\pm2.1\,\%$)
                \item non-prompt $\PDplus\to\Ptau$ ($0.02\pm0.02\,\%$)
                \item $X_{\Pbottom}\to\Ptau$ (meson or baryon) ($16.8\pm3.0\,\%$)
                \end{itemize}
              \item \textcolor{darkgreen}{use $\sigma(\Pbottom\APbottom)$ at $\unit{8}{\TeV}$ from LHCb}
              \item \textcolor{darkgreen}{use Pythia scaling for $\sigma(\Pcharm\APcharm)$ at $\unit{8}{\TeV}$}
        \end{itemize}
        \begin{exampleblock}{$\mathcal{B}(\PDplus\to\Ptau)$}
          \begin{itemize}
            \item old analysis: used upper limit
            \item now: $\mathcal{B}(\PDplus\to\Pmu\Pnum)$ + helicity suppression + phase space
            \item \texttt{hep-ex:0604043}
            \item $\mathcal{B}(\PDplus\to\Ptau\Pnut)=1\pm1\times10^{-4}$
          \end{itemize}
        \end{exampleblock}
      \end{frame}

      \begin{frame}
        \frametitle{Strategy}
        \begin{itemize}
          \item mostly as in many rare decay searches:
          \item loose stripping selection
          \item multivariate classification in: mass, PID, ``geometry/topology''
          \item relative normalisation ($\PDs\to\Pphi(\Pmu\Pmu)\Ppi$)
          \item invariant mass fit for expected background in each likelihood bin\newline fit in $m-m_{\Ptau}>\unit{30}{\MeV}$
          \item ``middle sidebands'' for classifier evaluation ($\unit{20}{\MeV}<m-m_{\Ptau}<\unit{30}{\MeV}$)
          \item CLs for limit calculation
        \end{itemize}
        \begin{block}{today}
          \begin{itemize}
            \item $\unit{3}{\reciprocal\femtobarn}$ analysis ``as is''
              \item nice \& interesting things not included so far e.g.\ 2011 reanalysed
          \end{itemize}
        \end{block}
      \end{frame}

      \begin{frame}
        \frametitle{Datasets}
        \begin{itemize}
          \item data from \textcolor{darkgreen}{Reco14Stripping20(r1)}
          \item much MC\begin{itemize}
              \item \textcolor{darkgreen}{24M} inclusive background events ($\Pbottom\APbottom$ and $\Pcharm\APcharm$)
              \item \textcolor{darkgreen}{10M} exclusive background events ($\PDs\to\Peta(\Pmu\Pmu\Pphoton)\Pmu\Pnum$)
              \item \textcolor{darkgreen}{2M} signal events (split over 5 production channels)
            \end{itemize}
          \item[$\Rightarrow$] \textcolor{darkgreen}{generator level cuts} for improved use of computing resources
            \begin{itemize}
              \item \textcolor{darkgreen}{$\sim 14$ times more} signal statistics after stripping
              \item \textcolor{darkgreen}{$\sim 2$ times more} background statistics
            \end{itemize}
          \item \textcolor{darkgreen}{mix $\Ptau$ production on ntuple level} instead of reweighting.
            \newline$\Rightarrow$ ease up ntuple usage (no forgotten weighting, no double weighting, \dots)
        \end{itemize}
      \end{frame}

      \begin{frame}
        \frametitle{(Stripping) selection}
{\footnotesize{
\begin{tabular}{|c|cc|}
\hline
 &$\Ptau\to\Pmu\Pmu\Pmu$&$\PDs\to\Pphi\Ppi$\\
\hline
$\mu^\pm$ , $ \pi^\pm$ &\multicolumn{2}{c|}{} \\
$p_T$                       &\multicolumn{2}{c|}{$>300\MeV$} \\
Track $\chi^2$/ndf        &\multicolumn{2}{c|}{$<3 $} \\
IP $\chi^2$/ndf           &\multicolumn{2}{c|}{$>9 $} \\
\textcolor{darkgreen}{track ghost probability}   &\multicolumn{2}{c|}{\textcolor{darkgreen}{$<0.3 $}} \\
\hline
$\mu$ pairs &\multicolumn{2}{c|}{} \\
$m_{\mu^+\mu^-} - m_{\phi}$ & $>20\MeV$ & $<20\MeV$\\
$m_{\mu^+\mu^-}$                       & $> 450\MeV$ & - \\
$m_{\mu^+\mu^+}$                       & $> 250\MeV$ & - \\
\hline
$\tau^\pm$ and \PDs        &\multicolumn{2}{c|}{} \\
$\Delta m$                & $<400\MeV$ & $<50\MeV$\\
Vertex $\chi^2$           &\multicolumn{2}{c|}{$<15$} \\
\textcolor{darkgreen}{IP $\chi^2$}               &\multicolumn{2}{c|}{$<225^1 $} \\
$\cos\alpha$              &\multicolumn{2}{c|}{$>0.99$} \\
$c\tau$ (stripping)       &\multicolumn{2}{c|}{$>\unit{100}{\mu m}$} \\
                          &\multicolumn{2}{c|}{no PV refitting}\\
decay time (offline)      &\multicolumn{2}{c|}{$> -0.01$ ns \& $< 0.025$ ns}\\
                          &\multicolumn{2}{c|}{PV refitting}\\
\hline
\end{tabular}
}}

{\footnotesize{$^1$ different LoKi functor}}
      \end{frame}

      \section{multivariate technique}

      \begin{frame}
        \frametitle{``geometric likelihood''}
        \begin{itemize}
            \item classify the displaced 3-body decay properties of a signal candidate
            \item revisit variable choice
            \item revisit classification technique
            \item \textcolor{darkgreen}{more toolkits tried: MatrixNet, NeuroBayes}, TMVA
            \item \textcolor{darkgreen}{retune input variables\newline($\PBs\to\Pmu\Pmu$ isolation $\rightarrow$ Laura's BDT isolation: CERN-THESIS-2013-259)}
        \end{itemize}

      \end{frame}
      \begin{frame}
        \frametitle{setup}
          \begin{itemize}
              \item train $1/3$ signal MC against $1/2$ background MC
              \item variables \begin{itemize}
                  \item $3\times$ DOCA
                  \item vertex $\chi^2$
                  \item $\tau$ decay time
                  \item $\tau$ IP$\chi^2$
                  \item min.\ $\mu$ IP$\chi^2$
                  \item $\Ptau$ pointing angle
                  \item $\tau$ $p_T$
                  \item max.\ track $\chi^2$
                  \item $\PBs\to\Pmu\Pmu$ track isolation
                  \item cone isolation
                  \item \textcolor{darkgreen}{BDT isolation}
                \end{itemize}
            \end{itemize}
          \end{frame}
          \begin{frame}
            \frametitle{futher tweaking}
            \begin{itemize}
                \item \textcolor{darkgreen}{remove fully reconstructed 3-body decays from background sample\newline (don't expect to be able to discriminate these)}
                  \item don't apply trigger prior to training
          \end{itemize}

        \begin{exampleblock}{``blending'' technique}
          \begin{itemize}
              \item for each signal channel we train: one BDT, three Fisher classifier, four MLPs, one FDA, and one LD classifier
              \item[$\Rightarrow$] 50 classifiers
                \item one final MatrixNet classifier using the 13 base variables and the 50 classifiers as input
                  \newline(trained on the second $1/3$ of signal MC and the second $1/2$ of background MC)
          \end{itemize}
        \end{exampleblock}
      \end{frame}

      \begin{frame}
        \frametitle{performance}
        \begin{itemize}
          \item classifier prefers $\Ptau$ from prompt $\PDs$
        \end{itemize}
        \begin{columns}
          \begin{column}{.48\textwidth}
            \begin{block}{MC response for different\newline $\Ptau$ production channels}
              \includegraphics[width=.95\textwidth]{./for_paul.png}
            \end{block}
          \end{column}
          \begin{column}{.48\textwidth}
            \begin{block}{response for $\PDs\to\Pphi\Ppi$\newline data and MC}
              \includegraphics[width=.95\textwidth]{./MN_BLEND_FLAT.png}
            \end{block}
          \end{column}
        \end{columns}
      \end{frame}

      \begin{frame}
        \frametitle{calibration}
        \begin{itemize}
          \item assume all differences between $\Ptau\to\Pmu\Pmu\Pmu$ and $\PDs\to\Pphi\Ppi$ come from kinematics (mass, resonance, decay time)
          \item get correction $\PDs\leadsto\Ptau$ from MC
          \item apply corrections to $\PDs\to\Pphi\Ppi$ on data
        \end{itemize}
        \begin{block}{validation}
          \begin{itemize}
              \item done for 2011 analysis, treating smeared MC as data
          \end{itemize}
        \end{block}
        \begin{columns}
        \begin{column}{.45\textwidth}
          \begin{itemize}
              \item $\PDs\to\Pphi\Ppi$ well modelled in MC
                \item until the very low likelihood end of the distribution
                \item[$\rightarrow$] i.e.\ also badly pointing non-prompt $\PDs$
          \end{itemize}
        \end{column}
        \begin{column}{.45\textwidth}
        \includegraphics[width=.95\textwidth]{MN_BLEND_FLAT.png}
        \end{column}
        \end{columns}
      \end{frame}

      \begin{frame}
        \frametitle{PID}
        \begin{itemize}
            \item we used ProbNNmu already in the previous round of the analysis
              \item now use MC12TuneV2 (latest)
              \item two-fold reason:\begin{itemize}
                    \item expect better performance than CombDLL variables
                      \item ``one variable for everything'':\newline with CombDLL we needed both CombDLL($\mu-\pi$) and CombDLL($\mu-K$)
                \end{itemize}
        \end{itemize}
      \end{frame}

      \begin{frame}
        \frametitle{PIDCalib}
        \begin{itemize}
            \item calibration strategy: use PIDCalib
              \item confirm with $\PDs\to\Pphi\Ppi$ if everything is fine
        \end{itemize}
        \begin{columns}
          \begin{column}{.45\textwidth}
            cut\&fit:
            \begin{itemize}
                \item fit $\PDs\to\Pphi\Ppi$ with a TIS muon in data
                  \item cut on ProbNNmu of one muon
                    \item fit again
                    \item[$\rightarrow$] ratio is ``true'' cut efficiency
            \end{itemize}
            \begin{block}{ProbNNmu>0.4}
              $\varepsilon=86.3\,\%$
            \end{block}
        \end{column}
        \begin{column}{.45\textwidth}
          PIDCalib
          \begin{itemize}
            \item apply full selection (incl. trigger) to $\PDs\to\Pphi\Ppi$ MC reference sample
            \item avoid IsMuon bias
          \end{itemize}
            \begin{block}{ProbNNmu>0.4}
              $\varepsilon=89.8\,\%$
            \end{block}
        \end{column}
        \end{columns}
      \end{frame}

      \begin{frame}
        \frametitle{?}
        \begin{itemize}
          \item first shown at \myhref{https://indico.cern.ch/event/291727/}{charming VRD meeting}
          \item also mentionned at last \myhref{https://indico.cern.ch/event/298021/}{LHCb week}
          \item many emails exchanged with Barbara Sciascia
          \item mistakes on user side found
          \item still no agreement
        \end{itemize}
        \begin{exampleblock}{phenomenologic treatment}
          \begin{itemize}
            \item correlations are small in $\PDs\to\Pphi\Ppi$ data and MC
              \newline $\varepsilon(\text{cut on one muon})^2 = \varepsilon(\text{cut on two muons})$
            \item[$\Rightarrow$] use $c^3=(\varepsilon(\text{cut and fit})/\varepsilon(\text{PIDCalib}))^3$ as correction to PIDCalib for $\Ptau\to\Pmu\Pmu\Pmu$
              \item assign error of $0.02$ for $c$
          \end{itemize}
        \end{exampleblock}
        \begin{itemize}
          \item planned: investigate further (usage/bug/samples)
          \item planned: use muons from $\PDs\to\Pphi\Ppi$ directly
        \end{itemize}
      \end{frame}

      \begin{frame}
        \frametitle{binning optimisation}
        \begin{itemize}
            \item how to optimise the binning in two classifiers?
          \item $\unit{1}{\reciprocal\femtobarn}$ CONF note: two one-dimensional optimisations as in $\PBs\to\Pmu\Pmu$
          \item $\unit{1}{\reciprocal\femtobarn}$ PAPER: iterative loop of one-dimensional optimisations\newline optimising one classifier on the sensitive range of the other classifier
          \item \textcolor{darkgreen}{now: optimise two-dimensions (optimise bin boundaries in both dimensions at the same time)}
            \item unchanged: don't use lowest likelihood bins\newline(reflection backgrounds, no sensitivity gain)
        \end{itemize}
      \end{frame}
      \begin{frame}
        \frametitle{impact of new binning optimisation}
        \begin{itemize}
          \item removal of tiny bins which contribute negligible sensitivity
            \item colour: limit obtained, using only this particular bin
              \item number: rank of that bin (1=best sensitivity bin)
        \end{itemize}
        
        ~

        \begin{columns}
          \begin{column}{.5\textwidth}
            old analysis

            ~

            \includegraphics[width=.95\textwidth]{./90CLonebinlimit.eps}
        \end{column}
        \begin{column}{.5\textwidth}
            new analysis
            
            (2011 data, not final calibration)
            \includegraphics[width=.95\textwidth]{./rank.eps}
        \end{column}
        \end{columns}
      \end{frame}


      \begin{frame}
        \frametitle{mass shape}
          \begin{itemize}
              \item double-Gaussian with fixed fraction ($70\,\%$ inner Gaussian)
                \item fix fraction to ease calibration:\newline
              $\sigma_{data}^{\Ptau} = \frac{\sigma_{MC}^{\Ptau}}{\sigma_{MC}^{\PDs}}\times\sigma_{data}^{\PDs}$
          \end{itemize}
       \includegraphics[width=.44\textwidth]{./Ds_data_2011.pdf}
       \includegraphics[width=.44\textwidth]{./Ds_data_2012.pdf}

       {\footnotesize{
         \begin{tabular}{|c|c|c|}
           \hline
           calibrated $\Ptau$ mass shape & 7~TeV & 8~TeV\\
           \hline
           Mean ($\MeV$) & $1779.1 \pm 0.1$ & $1779.0 \pm 0.1$\\
           \hline
           $\sigma_1$ ($\MeV$) & $7.7 \pm 0.1$ & $7.6 \pm 0.1$\\
           \hline
           $\sigma_2$ ($\MeV$) & $12.0 \pm 0.8$ & $11.5 \pm 0.5$\\
           \hline
         \end{tabular}
     }
   }
      \end{frame}

\beamertemplateshadingbackground{darkgreen!30}{PineGreen!30}
      \section{choice of triggers}
      \begin{frame}
        \frametitle{the story}
        \begin{itemize}
            \item first look at $\PDs\to\Pphi\Ppi$ revealed: signal/background much worse in 2012
            \item[$\rightarrow$] charm got more bandwidth in 2012
            \item taking all events quite unsatisfactory:\begin{itemize}
                  \item strictly speaking: we don't know why the events ended up on tape
                    \item trigger efficiencies unstable (TIS/TPS/TOS efficiencies for lots of lines and TCKs)
                      \item signal anyhow mostly TOS in a muon trigger
              \end{itemize}
            \item[$\Rightarrow$] if an event was background in the trigger, then don't consider it signal afterwards.
        \end{itemize}
      \end{frame}

      \begin{frame}
        \frametitle{strategy}
        \begin{itemize}
          \item back of the envelope: limit scales with $1/\varepsilon_\text{sig}$
          \item back of the envelope: limit scales with $\sqrt{\varepsilon_\text{bkg}}$
          \item[$\Rightarrow$] minimise $FOM=\frac{\sqrt{\varepsilon_\text{bkg}}}{\varepsilon_\text{sig}}$
          \item running TCKsh and some combinatorics: $\mathcal{O}(2^{256}\approx 10^{75})$ possibilities of using HLT2 line (ignoring that there are different TCKs, TIS/TOS/TPS/DEC, \dots)
          \item[$\rightarrow$] be more pragmatic!
        \end{itemize}
        \begin{exampleblock}{trigger optimisation}
          \begin{itemize}
            \item sort trigger lines (Punzi FoM)
            \item start with events from the best trigger line and compute $FOM$
            \item add events from the next trigger and recompute $FOM$
            \item iterate and converge to $FOM(\text{all triggers})$
            \item use all triggers until $FOM$ starts rising.
          \end{itemize}
        \end{exampleblock}
      \end{frame}

      \begin{frame}
        \frametitle{our triggers}
        {\footnotesize{
        \begin{tabular}{l|c|c}
            & signal & normalisation \\\hline\hline
            L0$^1$ & \multicolumn{2}{c}{L0Muon TOS}\\\hline
            Hlt1$^1$ & \multicolumn{2}{c}{Hlt1TrackMuon TOS}\\\hline
            Hlt2 2011 & Hlt2CharmSemilepD2HMuMu TOS & Hlt2DiMuonDetached$^2$ TOS \\
 & || Hlt2TriMuonTau TOS &   \\\hline
            Hlt2 2012 & Hlt2TriMuonTau$^1$ TOS & Hlt2DiMuonDetached$^2$ TOS\\\hline
          \end{tabular}
        }
      }
        \only<1>{
       \begin{block}{$^1$ triggers in 2012}
          \begin{itemize}
              \item cuts changed through 2012
              \item[$\rightarrow$] \textcolor{darkgreen}{emulated two different TCKs for 2012}
          \end{itemize}
        \end{block}}
      \only<2>{
        \begin{block}{$^2$ word on Hlt2DiMuonDetached}
          \begin{itemize}
            \item keep it simple here
            \item line unchanged in 2012
            \item[$\rightarrow$] choice keeps Hlt2 trigger efficiency stable
            \item $\PDs\to\Pphi\Ppi$ anyhow doesn't behave like $\Ptau\to\Pmu\Pmu\Pmu$ in the TriMuon trigger (requires misidentification)
          \end{itemize}
        \end{block}}
        
      \end{frame}

      \begin{frame}
        \frametitle{cross check (not in the note)}
        \begin{itemize}
            \item shouldn't the multivariate classifier do better than any trigger?
              \item back-of-the-envelope overestimates the improvement (expect $\sim 9\,\%$ improvement)
            \item[$\rightarrow$] add events back to the ntuple, recalculate normalisation, redid fits
            \item[$\Rightarrow$] \textcolor{darkgreen}{restricting the triggers gains $\sim 3\,\%$ sensitivity wrt.\ previous round}
        \end{itemize}
      \end{frame}
\beamertemplateshadingbackground{White}{White}

      \section{normalisation}

     \begin{frame}
       \frametitle{relative normalisation}
       $\mathcal{B}(\Ptau\to\Pmu\Pmu\Pmu) = \frac{\mathcal{B}(\PDs\to\Pphi\Ppi)}{\mathcal{B}(\PDs\to\Ptau\Pnut)} \times f_{\PDs}^{\Ptau} \times \frac{\varepsilon_\text{norm}    }{\varepsilon_\text{sig}     }  \times \frac{N_\text{sig}}{N_\text{norm}} = \alpha\times N_\text{sig}$
       \begin{itemize}
           \item where $\varepsilon$ stands for trigger, reconstruction, selection
           \item $\text{norm}$ = normalisation channel $\PDs\to\Pphi\Ppi$
           \item $f_{\PDs}^{\Ptau}$ is the fraction of $\Ptau$ coming from $\PDs$
             \newline i.e.\ $(83\pm3)\,\%$ for 2012
       \end{itemize}
       \includegraphics[width=.47\textwidth]{./Ds_data_2011.pdf}
       \includegraphics[width=.47\textwidth]{./Ds_data_2012.pdf}
     \end{frame}

     \begin{frame}[allowframebreaks]
       \frametitle{normalisation in numbers}
       {\footnotesize{
$\begin{array}{c|c|c}
& \rm{7~TeV} & \rm{8~TeV}\\
\hline
\rm{\epsilon\mathstrut_{sig}}^{GEN} & 8.98 \pm 0.41 & 9.20 \pm 0.36\\
\hline
\rm{\epsilon_{cal}}^{GEN} & 11.19 \pm 0.34 & 11.53 \pm 0.32\\
\hline
\rm{\epsilon_{sig}}^{REC,isMuon,SEL} & 9.794 \pm 0.028 & 9.134 \pm 0.023 \\
\hline
\rm{\epsilon_{cal}}^{REC,isMuon,SEL} & 7.187 \pm 0.022 & 6.690 \pm 0.022 \\
\hline 
\frac{\rm{c_{cal}}^{track}}{\rm{c_{sig}}^{track}} & 0.997 \pm 0.009 \pm 0.026 & 0.996 \pm 0.009 \pm 0.026 \\
\hline
\frac{\rm{c_{cal}}^{\mu ID}}{\rm{c_{sig}}^{\mu ID}} & 0.9731 \pm 0.0031 \pm 0.0264 & 1.0071 \pm 0.0022 \pm 0.0204 \\
\hline
\rm{c}^{\Pphi} & \multicolumn{2}{c}{0.98 \pm 0.01} \\
\hline
\rm{c}^{\Ptau} & 1.032 \pm 0.006 & 1.026 \pm 0.006\\
\hline 
\rm{c}^{trash} & 1.95 \pm 0.12 & 2.05 \pm 0.12\\
\hline
\rm{\epsilon\mathstrut_{sig}}^{TRIG} & 35.45 \pm 0.11 \pm 0.14 & 39.1 \pm 1.7 \pm 2.0 \\
\hline
\rm{\epsilon\mathstrut_{cal}}^{TRIG} & 23.42 \pm 0.14 \pm 0.09 & 20.62 \pm 0.76 \pm 1.07 \\
\end{array}$ 
       }}

\framebreak

       {\footnotesize{
$\begin{array}{c|c|c}
& \rm{7~TeV} & \rm{8~TeV}\\
\hline
\mathcal{B}(\PDs\to\Pphi\Ppi) & \multicolumn{2}{c}{(1.317 \pm 0.099) \times 10^{-5}}\\
\hline
f^{\Ptau}_{\PDs} & 0.82 \pm 0.03 & 0.83 \pm 0.03 \\
\hline
\mathcal{B}(\PDs\to\Ptau\Pnut) & \multicolumn{2}{c}{0.0561 \pm 0.0024}\\
\hline
\rm{\epsilon\mathstrut_{norm}}^{REC\&SEL}/
\rm{\epsilon\mathstrut_{sig}}^{REC\&SEL}
& 0.897 \pm 0.061 & 0.926 \pm 0.056 \\  
\hline
\rm{\epsilon\mathstrut_{norm}}^{TRIG}/
\rm{\epsilon\mathstrut_{sig}}^{TRIG}  
& 0.6606 \pm 0.0059 & 0.527 \pm 0.041\\  
\hline
N_{norm} & 28,162 \pm 434 & 51,998 \pm 684\\
\hline & \\[-1.5em]\hline
\alpha & (4.05 \pm 0.48) \times 10^{-9} & (1.83 \pm 0.25) \times 10^{-9}\\
\alpha^{trash} & (7.90 \pm 0.49) \times 10^{-9} & (3.75 \pm 0.27) \times 10^{-9}\\
\end{array}$
}}
     \end{frame}


      \section{backgrounds}

      \begin{frame}
        \frametitle{misidentification}
        \begin{itemize}
          \item most dominant: $\PDplus\to\PK\Ppi\Ppi$
          \item experience from last round: cut away low ProbNNmu range
          \item check remaining data under $\PK\Ppi\Ppi$ hypothesis for $\PDplus$ peak
          \item[$\Rightarrow$] misid safely contained in ``trash'' bin
          \item \textcolor{darkgreen}{$\PDplus\to\Ppi\Ppi\Ppi$ and $\PDs\to\Ppi\Ppi\Ppi$ start to become visible in 2012}
        \end{itemize}
        \includegraphics[width=.45\textwidth]{./Dp2Kpipi_all_2012_senseBins.pdf}
        \includegraphics[width=.45\textwidth]{./FittoD23pi_2012.pdf}
      \end{frame}

      \begin{frame}
        \frametitle{evil backgrounds}
        \begin{itemize}
          \item $\Pphi\to\Pmu\Pmu + X$: narrow veto on dimuon mass
          \item $\PDs\to\Peta(\Pmu\Pmu\Pphoton)\Pmu\Pnum$: not so easy
            \begin{itemize}
              \item  modelled in CONF note
              \item optimised veto in PAPER
              \item right now: both versions in the ANA note
            \end{itemize}
          \item baseline: veto $m_{\APmuon\Pmuon} < \unit{450}{\MeV}$
            \begin{itemize}
              \item fits better understood
              \item sensitivity unchanged when removing veto
              \item smaller uncertainty on expected background
            \end{itemize}
        \end{itemize}
      \end{frame}

      \begin{frame}
        \frametitle{remaining backgrounds}
        \begin{itemize}
            \item fit exponential to invariant mass spectrum in each likelihood bin
            \item don't use $\pm \unit{30}{\MeV}$ in the fit
            \item[$\rightarrow$] compatible results blinding only $\pm \unit{20}{\MeV}$\footnote{partially used in classifier developement}
        \end{itemize}
        {\begin{center}
          most sensitive bins in 2011 and 2012
          \includegraphics[width=.4\textwidth]{./fit2011.png}
          \includegraphics[width=.4\textwidth]{./fit2012.png}
          \end{center}}
      \end{frame}

      \section{results}

      \begin{frame}
        \frametitle{expected limit}
        \begin{itemize}
            \item still blinded
              \item consider nuisance parameters from background fit, signal pdf calibration, normalisation
              \item nuisance parameters due to $\Ptau$ production not included in signal pdf shape, yet
                \item limit for combined 2011+2012 analysis
        \end{itemize}
      \end{frame}

      \begin{frame}
        \frametitle{sensitivity}
        $\mathcal{B}(\Ptau\to\Pmu\Pmu\Pmu)<5.6 \times 10^{-8}$ at 90\% CL

        \includegraphics[width=.8\textwidth]{./banana.png}
      \end{frame}

\beamertemplateshadingbackground{PineGreen!30}{White}
      \section{model dependence}

      \begin{frame}
        \frametitle{model dependence}
        \begin{itemize}
          \item $\Peta$ veto $\Rightarrow$ our limit not applicable to New Physics with small $m_{\APmuon\Pmuon}$
          \item model independent description in \texttt{arXiv:0707.0988} 
            \item 5 relevant Dalitz distributions: 2 four-point operators, 1 radiative operator, 2 interference terms
          \end{itemize}
          \only<2->{
            \begin{itemize}
              \item with radiative distribution limit gets worse by $51\,\%$ (dominantly from the $\Peta$ veto)
               \item the other four Dalitz distributions behave nicely (within $7\,\%$)
        \end{itemize}
      }
        \only<1>{
  \includegraphics[width=.331\textwidth]{./gammallll.eps}
  \includegraphics[width=.331\textwidth]{./gammallrr.eps}
  \includegraphics[width=.331\textwidth]{./gammarad.eps}

  \includegraphics[width=.331\textwidth]{./gammarad-llll.eps}
  \includegraphics[width=.331\textwidth]{./gammarad-llrr.eps}
}

      \end{frame}

\beamertemplateshadingbackground{White}{White}

      \begin{frame}
        \frametitle{Conclusion}
        \begin{columns}
          \begin{column}{.55\textwidth}
        \begin{itemize}
            \item finally all pieces put together
              \item model (in)dependence of $\Peta$ veto investigated
              \item expected sensitivity computed\newline $5.6\times 10^{-8}$
        \end{itemize}
        \end{column}
        \begin{column}{.45\textwidth}
          \includegraphics[width=\textwidth]{party-music-hd-wallpaper-1920x1200-3850.jpg}
        \end{column}
        \end{columns}

      \end{frame}
      \end{document}
