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\author{ {\fontspec{Trebuchet MS}Marcin Chrz\k{a}szcz, Katharina M\"{u}eller} (UZH)}
\institute{UZH}
\title[Low Mass Drell-Yan Status Report ]{Low Mass Drell-Yan Status Report }
\date{7 September 2015}
\begin{document}
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\begin{center}
\begin{center}
\begin{columns}
\begin{column}{0.75\textwidth}
\flushright\fontspec{Trebuchet MS}\bfseries \Huge {Low Mass Drell-Yan Status Report }
\end{column}
\begin{column}{0.02\textwidth}
{~}
\end{column}
\begin{column}{0.23\textwidth}
% \hspace*{-1.cm}
\vspace*{-3mm}
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\quad
\vspace{3em}
\begin{columns}
\begin{column}{0.44\textwidth}
\flushright \vspace{-1.8em} {\fontspec{Trebuchet MS} \Large Marcin Chrząszcz\\\vspace{-0.1em} Katharina M\"{u}eller Nicola Chiapolini}
\end{column}
\begin{column}{0.53\textwidth}
\includegraphics[height=1.3cm]{uzh-transp}
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\vspace{1em}
% \footnotesize\textcolor{gray}{With N. Serra, B. Storaci\\Thanks to the theory support from M. Shaposhnikov, D. Gorbunov}\normalsize\\
\vspace{0.5em}
\textcolor{normal text.fg!50!Comment}{QEE WG, CERN\\June 9, 2016}
\end{center}
\end{frame}
}
\begin{frame}\frametitle{Introduction to Drell-Yan}
\begin{columns}
\column{2.5in}
\begin{itemize}
\item Drell-Yan are process of two quark anihilations in which neutral current couples to two leptons.
\item The cross section of this process depends on two components:
\begin{itemize}
\item Hard scattering process $\color{OrangeRed}{\Rrightarrow}$ NNLO pQCD.
\item Parton Distribution Function (PDF).
\end{itemize}
\item Measurement of the cross section have a high sensitivity to the PDF
\item Due to unique coverage $2<y<5$ LHCb probes the $Q^2-x$ region not covered by other experiments.
\end{itemize}
\column{2.5in}
\includegraphics[width=0.95\textwidth]{images/feynmanDiagram_DrellYan_wRad.png}\\
\includegraphics[width=0.85\textwidth]{images/Q2_x.png}
\end{columns}
\end{frame}
\begin{frame}\frametitle{Selection}
\begin{itemize}
\item Main topic of Nicolas PhD.
\item Analysis based on 2011 data set.
\item Trigger:
\begin{itemize}
\item \texttt{L0\_L0DiMuonDecision},
\item \texttt{Hlt1DiMuonHighMassDecision},
\item \texttt{Hlt2DiMuonDY(3,4)Decision}
\end{itemize}
\item Stripping:
\begin{itemize}
\item \texttt{StrippingDY2MuMuLine(3,4)}
\end{itemize}
\item Selection:
\begin{itemize}
\item $2<\eta^{\mu}<4.5$,
\item $p^{\mu} > 10~\GeV$,
\item $p_T^{\mu} > 3~\GeV$,
\item $\chi^{2,\mu\mu}_{vtx}<5$,
\item $10< m(\mu\mu) < 120~\GeV$.
\end{itemize}
\end{itemize}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}\frametitle{Isolation}
\begin{itemize}
\item Drell-Yan unfortunately do not peak in mass $\twoheadrightarrow$ need another variable to control the purity.
\item Find mass independent isolation variable such that the signal template can be determined from data.
\item We define an isolation variable:
\begin{align*}
\mu_{ {\rm{iso}}} = \log(p_T^{ cone}(\mu, 0.5) - p_T^{ cone}(\mu, 0.1))
\end{align*}
\item For two muons we take the maximum of the two isolations:
\begin{align*}
\mu\mu_{ {\rm{iso}}} = \max( \mu_{ {\rm{iso}}}^+, \mu_{ {\rm{iso}}}^-)
\end{align*}
\end{itemize}
\begin{center}
\begin{columns}
\column{0.5\textwidth}
\includegraphics[angle=-90,width=0.9\textwidth]{images/Z0_iso.pdf}
\column{0.5\textwidth}
\includegraphics[width=0.8\textwidth]{images/iso.png}
\end{columns}
\end{center}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}\frametitle{Isolation mass dependence}
\begin{itemize}
\item Unfortunately the $\mu\mu_{iso}$ is showing some mass dependence:
\end{itemize}
\begin{center}
\includegraphics[width=0.75\textwidth]{images/DY.png}
\end{center}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}\frametitle{Signal template}
\begin{columns}
\column{2.7in}
\begin{itemize}
\item We do not want to use MC for determination of the signal $\mu\mu_{iso}$ template.
\item We adopted a data driven procedure:
\begin{itemize}
\item The template is taken from data and scaled to account for $\mu\mu_{iso}$ mass dependence.
\end{itemize}
\begin{itemize}
\item Take the \textit{Splot} $\PZ \to \mu \mu$ from data and multiply it by the scale factor determined from minimalising the $\chi^2$ between MC $\PZ$ and DY in particular region.
\end{itemize}
\end{itemize}
\column{2.3in}
\includegraphics[width=0.9\textwidth]{images/result_Z0.png}\\
\includegraphics[width=0.9\textwidth]{{images/3.0_3.25_10500.0_12000.0Nicola}.png}
\end{columns}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}\frametitle{Signal template - Summary}
\begin{itemize}
\item We are investigating the impact on the analysis for the different approaches
\item For now it looks like the results do not change with using different signal templates.
\item Because templates are data driven we need to ensure a large statistics in each of the $m_{\mu\mu},~y$ bins, because of this the last $y$ bin is larger then the rest.
\end{itemize}
\begin{center}
\includegraphics[width=0.5\textwidth]{images/scalef.png}
\end{center}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}\frametitle{Backgrounds}
\begin{itemize}
\item There are two sources of backgrounds:
\begin{itemize}
\item Heavy flavour decays.
\item Mis-ID.
\end{itemize}
\item For fitting the $\mu\mu_{iso}$ we need to know both the signal and background distribution.
\item Background templates can be determined from data
\begin{itemize}
\item Heavy flavour decays:\\
$\looparrowright$ Requiring the $\chi^{2,\mu\mu}_{vtx}>16$\\
$\looparrowright$ For cross-check $\rm IP>5~\rm mm$
\item Miss-ID:\\
$\looparrowright$ Require that both muons have the same sign.\\
$\looparrowright$ For cross-check take the minimum bias stripping line.
\end{itemize}
\end{itemize}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}\frametitle{Over all fits}
\begin{columns}
\column{3in}
\begin{itemize}
\item Using the above 3 mentioned templates the fits converge without any problems.
\item The higher one goes in mass the cleaner the signal is.
\end{itemize}
%\includegraphics[angle=-90,width=0.45\textwidth]{{images/Fits/12000_15000_y_bin_3.5_4.5_12}.pdf}
\begin{small}
{~}{~}{~}\begin{tabular}{|c|c|}
\hline
Mass bin & Purity \\ \hline
$[40,60]~\GeV$ & $0.879 \pm 0.019$\\
$[30,40]~\GeV$ & $0.754 \pm 0.015$\\
$[25,30]~\GeV$ & $0.657 \pm 0.011$\\
$[20,25]~\GeV$ & $0.507 \pm 0.008$\\
$[17.5,20]~\GeV$ & $0.402 \pm 0.007$\\
$[15,17.5]~\GeV$ & $0.316 \pm 0.006$\\ \hline
\end{tabular}
\end{small}
\column{2in}
\includegraphics[angle=-90,width=0.9\textwidth]{{images/Fits/12_12000_15000_y_bin_2_2.25_12}.pdf}\\
\includegraphics[angle=-90,width=0.9\textwidth]{{images/Fits/12_15000_20000_y_bin_3.5_4.5_12}.pdf}
\end{columns}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}\frametitle{Cross section calculations}
\begin{itemize}
\item To calculate the cross section the luminosity will be used:
\end{itemize}
\begin{align*}
\sigma= \dfrac{\varrho f^{{\rm MIG}}}{\mathcal{L} \varepsilon^{{\rm SEL}}} \sum \dfrac{1}{\varepsilon^{{\rm TRIG}} \varepsilon^{{\rm MUID}} \varepsilon^{{\rm GEC}} \varepsilon^{{\rm TRACK}}},
\end{align*}
where\\
\begin{itemize}
\item $\varrho$ signal fraction from the fit.
\item $f^{{\rm MIG}}$ correction to bin-bin migration.
\item $\mathcal{L}$ integrated luminosity.
\item $\varepsilon^{{\rm SEL}}$ efficiency on the vertex requirement.
\item $\varepsilon^{{\rm MUID}}$ muon identification efficiency.
\item $\varepsilon^{{\rm GEC}}$ global event cut efficiency.
\item $\varepsilon^{{\rm TRACK}}$ tracking efficiency.
\end{itemize}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}\frametitle{Cross section calculations}
\begin{itemize}
\item To calculate the cross section the luminosity will be used:
\end{itemize}
\begin{align*}
\sigma= \dfrac{ {\color{ForestGreen}{\varrho}} f^{{\rm MIG}}}{{\color{ForestGreen}{\mathcal{L}}} {\color{ForestGreen}{ \varepsilon^{{\rm SEL}}}}} \sum \dfrac{1}{\varepsilon^{{\rm TRIG}} \varepsilon^{{\rm MUID}} {\color{ForestGreen}{\varepsilon^{{\rm GEC}}}} \varepsilon^{{\rm TRACK}}},
\end{align*}
where\\
\begin{itemize}
\item $ {\color{ForestGreen}{\varrho}}$ signal fraction from the fit.
\item $f^{{\rm MIG}}$ correction to bin-bin migration.
\item $ {\color{ForestGreen}{\mathcal{L}}}$ integrated luminosity.
\item $ {\color{ForestGreen}{\varepsilon^{{\rm SEL}}}}$ efficiency on the vertex requirement.
\item $\varepsilon^{{\rm MUID}}$ muon identification efficiency.
\item $ {\color{ForestGreen}{\varepsilon^{{\rm GEC}}}}$ global event cut efficiency.
\item $\varepsilon^{{\rm TRACK}}$ tracking efficiency.
\end{itemize}
\ARROW \color{ForestGreen}{Done}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}\frametitle{$ {\color{ForestGreen}{\varepsilon^{{\rm SEL}}}}$}
\ARROW Evaluated using MC sample:\\{~}\\
\begin{center}
\begin{tabular}{|c|c|}
\hline
$2011$ MagDown & $0.21320 \pm 0.00014$ \\
$2011$ MagUp & $0.21306 \pm 0.00014$ \\
$2012$ MagDown & $0.20402 \pm 0.00013$ \\
$2012$ MagUp & $0.20372 \pm 0.00013$ \\
\hline
\end{tabular}
\end{center}
\ARROW Good agreement between polarities!\\
\ARROW $2012$ efficiency is lower then the $2011$.\\
\ARROW Will merge the polarities:
\begin{center}
\begin{tabular}{|c|c|}
\hline
$2011$ & $0.21313 \pm 0.00010$ \\
$2012$ & $0.20387 \pm 0.00009$ \\
\hline
\end{tabular}
\end{center}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}\frametitle{$ {\color{ForestGreen}{\varepsilon^{{\rm GEC}}}}$}
\ARROW Evaluated on data directly, by fitting the $\Gamma( {\rm SPDHits})$ to data:\\{~}\\
\begin{columns}
\column{0.1in}
{~}\\
\column{0.45\textwidth}
\ARROW $2011$ data:
\includegraphics[width=0.95\textwidth]{{images/spdhits_11_y_2_4.5_10500_60000}.png}
\column{0.45\textwidth}
\ARROW $2012$ data:
\includegraphics[width=0.95\textwidth]{{images/spdhits_12_y_2_4.5_10500_60000}.png}
\end{columns}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}\frametitle{$ {\color{ForestGreen}{\varepsilon^{{\rm GEC}}}}$}
\ARROW Testing the $y - M_{\mu\mu}$ dependence:\\{~}\\
\begin{columns}
\column{0.1in}
{~}\\
\column{0.45\textwidth}
\ARROW $2011$ data\\ $y \in(2,2.25)$\\ $M_{\mu\mu} \in (10.5,12)~\GeV$ :
\includegraphics[width=0.95\textwidth]{{images/spdhits_11_y_2_2.25_10500_12000}.png}
\column{0.45\textwidth}
\ARROW $2012$ data\\ $y \in(2,2.25)$\\ $M_{\mu\mu} \in (10.5,12)~\GeV$ :
\includegraphics[width=0.95\textwidth]{{images/spdhits_12_y_2_2.25_10500_12000}.png}
\end{columns}
\ARROW We didn't observe a variation of the efficiency as a function of $\M_{\mu\mu}$ and $y$.
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}\frametitle{$ {\color{ForestGreen}{\varepsilon^{{\rm GEC}}}}$}
\ARROW Proposed a systematic:{~}\\
\begin{columns}
\column{0.1in}
{~}\\
\column{0.45\textwidth}
\ARROW $2011$ data:
\includegraphics[width=0.95\textwidth]{{images/eff11}.png}
\column{0.45\textwidth}
\ARROW $2012$ data:
\includegraphics[width=0.95\textwidth]{{images/eff12}.png}
\end{columns}
{~}\\
\ARROW Suggest the RMS as small systematic.
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}\frametitle{Conclusions}
\ARROW The analysis was delayed due to lack of my time :(\\
\ARROW I have stooped teaching so I expect much more time to continue this.\\
\ARROW The remaining corrections could be taken from the $\PZzero \to \mu \mu$ analysis.
\end{frame}
\backupbegin
\begin{frame}\frametitle{Backup}
\topline
\end{frame}
\backupend
\end{document}
Marcin Chrzaszcz