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\author{ {\fontspec{Trebuchet MS}Marcin Chrz\k{a}szcz} (Universit\"{a}t Z\"{u}rich)}
\institute{UZH}
\title[Extracting angular observables with Method of Moments]{Extracting angular observables with Method of Moments}
\date{5-7 October 2015}
\begin{document}
\tikzstyle{every picture}+=[remember picture]
{
\setbeamertemplate{sidebar right}{\llap{\includegraphics[width=\paperwidth,height=\paperheight]{bubble2}}}
\begin{frame}[c]%{\phantom{title page}}
\begin{center}
\begin{center}
\begin{columns}
\begin{column}{0.9\textwidth}
\flushright\fontspec{Trebuchet MS}\bfseries \Huge {Extracting angular observables with Method of Moments}
\end{column}
\begin{column}{0.2\textwidth}
%\includegraphics[width=\textwidth]{SHiP-2}
\end{column}
\end{columns}
\end{center}
\quad
\vspace{3em}
\begin{columns}
\begin{column}{0.44\textwidth}
\flushright \vspace{-1.8em} {\fontspec{Trebuchet MS} \Large Marcin Chrząszcz\\\vspace{-0.1em}\small \href{mailto:mchrzasz@cern.ch}{mchrzasz@cern.ch}}
\end{column}
\begin{column}{0.53\textwidth}
\includegraphics[height=1.3cm]{uzh-transp}
\end{column}
\end{columns}
\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}{In collaboration with F.Beaujean, D.van Dyk, N.Serra \\Novel aspects of $\Pbeauty \to \Pstrange$ transitions,\\ Marseille, October 5-7, 2015}
\end{center}
\end{frame}
}
\section{Motivation}
\begin{frame}\frametitle{Motivation}
Likelihood(LL) fits even though widely used suffer from couple of draw backs:
\begin{enumerate}
\item In case of small number events LL fits suffer from convergence problems. This behaviour is well known and was observed several times in toys for $\PB \to \PKstar \Pmu \Pmu$.
\item LL can exhibit a bias when underlying physics model is not well known, incomplete or mismodeled.
\item The LL have problems converging when parameters of the \pdf are close to their physical boundaries.%, so-called ''boundary problem''
\item Accessing uncertainty in LL fits sometimes requires application of computationally expensive Feldman-Cousins method.
\end{enumerate}
\end{frame}
\begin{frame}\frametitle{Method of Moments}
\begin{columns}
\column{0.05in}{~}
\column{2.2in}
\begin{center} MoM addresses the above problems:\end{center}
\column{2.in}
\only<3>{
\begin{center} Drawback:\end{center}
}
\end{columns}
\begin{columns}
\column{0.05in}{~}
\column{2.2in}
\only<1>{
%\begin{center} MoM solves the above problems:\end{center}
\begin{exampleblock}{Advantages of MoM}
\begin{itemize}
\item Probability distribution function rapidity converges towards the Gaussian distribution.
\item MoM gives an unbias result even with small data sample.
\item Insensitive to large class of remodelling of physics models.
\item Is completely insensitive to boundary problems.
\end{itemize}
\end{exampleblock}
}
\only<2,3>{
%\begin{center} MoM solves the above problems:\end{center}
\begin{exampleblock}{Advantages of MoM}
\begin{itemize}
\item "For each observable, the mean value can be determined independently from all other observables.
\item Uncertainly follows perfectly $1/\sqrt{N}$ scaling, where N is number of signal events.
\end{itemize}
\end{exampleblock}
}
\column{2.in}
\only<3>{
%\begin{center} Drawback:\end{center}
\begin{alertblock}{Advantages of MoM}
\begin{itemize}
\item Estimated uncertainty in MoM is larger then the ones from LL.
\end{itemize}
\end{alertblock}
}
\end{columns}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Method of Moments}
\begin{frame}\frametitle{Introduction to MoM}
Let us a define a probability density function \pdf of a decay:
\begin{align}
P(\nuvec, \thvec) \equiv \sum_i S_i(\nuvec) \times f_i(\thvec)
\end{align}
Let's assume further that there exist a dual basis: $\lbrace f_i(\thvec) \rbrace$, $\{\dual{f}_i(\thvec)\}$ that the orthogonality relation is valid:
\begin{equation}
\label{eq:def-ortho-rel}
\int_\Omega \rmdx{\vec{\theta}} \dual{f}_i(\thvec) f_j(\thvec) = \delta_{ij}
\end{equation}
Since we want to use MoM to extract angular observables it's normal to work with Legendre polynomials. In this case we can find self-dual basis:
\begin{equation}
\forall_i \dual{f}_i = f_i~,
\end{equation}
just by applying the ansatz: $\dual{f}_i=\sum_i a_{ij} f_j$.
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}\frametitle{Determination of angular observables}
Thanks to the orthonormality relation Eq.~\ref{eq:def-ortho-rel} one can calculate the $S_i(\nuvec)$ just by doing the integration:
\begin{align}
S_i(\nuvec)=\int_\Omega d \thvec P(\nuvec, \thvec) \dual{f}_i(\thvec)
\end{align}
\pause
We also need to integrate out the $\nuvec$ dependence:
\begin{align}\label{eq:canonical}
\langle S_i \rangle= \int_\Theta d \nuvec \int_\Omega d \thvec P(\nuvec, \thvec) \dual{f}_i(\thvec)
\end{align}
\pause
MoM is basically performing integration in~Eq.~\ref{eq:canonical} using MC method:
\begin{align*}
E[S_i] \to \widehat{E[S_i]}=\dfrac{1}{N}\sum_{k=1}^{N} \dual{f}(x_k)
\end{align*}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}\frametitle{Uncertainty estimation}
MoM provides also a very fast and easy way of estimating the statistical uncertainty:
\begin{align}
\sigma (S_i)= \sqrt{\dfrac{1}{N-1}\sum_{k=1}^N ( \dual{f}_i(x_k) - \widehat{S_i} )^2 }
\end{align}
and the covariance:
\begin{equation}
\mathrm{Cov} [S_i, S_j]=\dfrac{1}{N-1} \sum_{k=1}^N [ \widehat{S_i} - \dual{f}_i(x_k) ][ \widehat{S_j} - \dual{f}_j(x_k) ]
\end{equation}
%\pause
%Thanks to the CLT both equations are satisfied.
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Systematic uncertainties}
%\placelogofalse
\begin{frame}\frametitle{Partial Waves mismodeling}
\begin{columns}
\column{3in}
\only<1>
{
\begin{itemize}
\item Let us consider a decay of $\PB \to P_1 P_2 \Pmuon \APmuon$.
\item In terms of angular \pdf is expressed in terms of partial-wave expansion.
\item For $\PB \to \PK \Ppi \Pmuon \APmuon$ system, S,P,D waves have been studied.
\end{itemize}
}
\only<2>
{
\begin{itemize}
\item One can write the \pdf separating the hadronic system:
\end{itemize}
\begin{align}
P(\cos \theta_1, & \cos \theta_2, \theta_3) = \\ &\quad \nonumber \sum _i S_i(\nuvec, \cos \theta_2) f_i (\cos \theta_1, \theta_3)
\end{align}
}
\column{2in}
\includegraphics[width=0.9\textwidth]{images/fig-topology.pdf}
\end{columns}
\only<1>{
\begin{itemize}
\item The muon system of this kind of decays has a fixed angular dependence in terms of $\theta_1$ (lepton helicity angle) and $\theta_3$ (azimuthal angle).
\item The hadron system can have arbitrary large angular momentum.
\end{itemize}
}
\only<2>{
\begin{itemize}
\item $S_i(\nuvec, \cos \theta_2)$ can be further expend in terms of Legendre polynomials $p_l^{\vert m \vert }(\cos \theta_2)$:
\end{itemize}
\begin{align}
S_i(\nuvec, \cos \theta_2) = \sum_{l=0}^{\inf} S_{k,l}(\nuvec ) p_l^{\vert m \vert}(\cos \theta_2)
\end{align}
\begin{small}
\begin{itemize}
\item Experimentally the $ S_{k,l}$ are easily accessible, but there is a theoretical difficulty as one would need to sum over infinite number of partial waves.
\end{itemize}\end{small}
}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}\frametitle{Detector effects}
\begin{columns}
\column{2.5in}
\begin{itemize}
\item Since our detectors are not a perfect devices the angular distribution observed by them are not the distributions that the physics model creates.
\end{itemize}
\column{2.5in}
\includegraphics[width=0.7\textwidth, angle=-90]{images/Fig2b.pdf}
\end{columns}
\pause
\begin{itemize}
\item To take into account the acceptance effects one needs to simulate the a large sample of MC events.\\
\item Try to figure out the efficiency function.
\item Number of possibilities.
\item Then you can just weight events:
\end{itemize}
\begin{align*}
\widehat{E[S_i]}=\dfrac{1}{\sum_{k=1}^N w_k}\sum_{k=1}^{N} w_k \dual{f}(x_k),~w_k=\dfrac{1}{\epsilon(x_k)}
\end{align*}
\end{frame}
\begin{frame}\frametitle{Unfolding matrix}
\only<1>{
In general one can write the distribution of events after the detector effects:
\begin{align}
P^{\rm{Det}}(x_d) = N \int \int dx_t~ P^{\rm{Phys}}(x_t) E(x_d \vert x_t),
\end{align}
where $N^{-1}=\int \int d x_t~ dx_d~ P^{\rm{Phys}}(x_t) E(x_d \vert x_t)$ and $E(x_d \vert x_t)$ denotes the efficiency $\epsilon(x_t)$ and resolution of the detector $ R(x_d\vert x_t)$:
\begin{align}
E(x_d \vert x_t) = \epsilon(x_t) R(x_d\vert x_t)
\end{align}
%\pause
One can define the raw moments:
\begin{align}
Q_i^{(m)} = \int \int d x_t~ dx_d~\dual{f}_i(x_d) P^{(m)}(x_t) E(x_d \vert x_t)
\end{align}
\begin{align}
M_{ij} = \begin{cases}
2 Q_i^{(0)} & j = 0\,,\\
2\left(Q_i^{(j)} - Q_i^{(0)}\right) & j \neq 0\,,\\
\end{cases}
\end{align}
Once we measured the moments $Q$ in data we can invert Eq. 11 and get the $\vec{S}$: $\widehat{\vec{S}}=M^{-1} \widehat{\vec{Q}}.$
}
\end{frame}
\section{Toy Studies}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}\frametitle{Toy Validation}
\begin{columns}
\column{2.5in}
\begin{itemize}
\item All the statistics properties of MoM have been tested in numbers of TOY MC.
\item As long as you have~$\sim 30$ events your pulls are perfectly gaussian.
\item Uncertainty scales with $\frac{\alpha}{\sqrt{n}}$, $\alpha = \mathcal{O}(1)$.
\item Never observed any boundary problems.
\end{itemize}
\column{2.5in}
\includegraphics[width=0.85\textwidth]{images/pull-Q2_5_6_S5_200.png}\\
\includegraphics[angle =-90, width=0.85\textwidth]{images/Q2_1_2_S5.pdf}
\end{columns}
\end{frame}
\begin{frame}\frametitle{Correlation of MoM with Likelihood}
{
\begin{columns}
\column{2.5in}
\begin{itemize}
\item MoM is highly correlated with LL.
\item Despite the correlation there can be difference of the order of statistical error.
\end{itemize}
\column{2.5in}
\includegraphics[angle=-90,width=0.85\textwidth]{images/S3_scat.pdf}\\
\end{columns}
}
\begin{columns}
\column{2.5in}
\includegraphics[angle=-90,width=0.85\textwidth]{images/S5_scat.pdf}
\column{2.5in}
\includegraphics[angle=-90,width=0.85\textwidth]{images/S7_scat.pdf}
\end{columns}
\end{frame}
\section{Conclusions}
\begin{frame}\frametitle{Conclusions}
\begin{enumerate}
\item MoM viable alternative to LL fits.
\item Allows LHCb to go smaller $q^2$ bins (get ready for $1~\GeV^2$ soon!).
\item Alternative method of extracting the detector effects.
\item Method is universally applicable, as long as an orthonormal
basis for the \pdf exists.
\end{enumerate}
\end{frame}
\begin{frame}
\begin{Huge}
BACKUP
\end{Huge}
\end{frame}
\begin{frame}\frametitle{LHCb detector}
\begin{columns}
\column{3.in}
\begin{center}
\includegraphics[width=0.98\textwidth]{det.jpg}
\end{center}
\column{2.0in}
\begin{footnotesize}
LHCb is a forward spectrometer:
\begin{itemize}
\item Excellent vertex resolution.
\item Efficient trigger.
\item High acceptance for $\Ptau$ and $\PB$.
\item Great Particle ID
\end{itemize}
\end{footnotesize}
\end{columns}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\backupbegin
\begin{frame}\frametitle{Backup}
\end{frame}
\backupend
\end{document}
mchrzasz