\documentclass[xcolor=svgnames]{beamer} \usepackage[utf8]{inputenc} \usepackage[english]{babel} \usepackage{polski} %\usepackage{amssymb,amsmath} %\usepackage[latin1]{inputenc} %\usepackage{amsmath} %\newcommand\abs[1]{\left|#1\right|} \usepackage{amsmath} \newcommand\abs[1]{\left|#1\right|} \usepackage{hepnicenames} \usepackage{hepunits} \usepackage{color} \setbeamertemplate{footline}{\insertframenumber/\inserttotalframenumber} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 \definecolor{mygreen}{cmyk}{0.82,0.11,1,0.25} \usetheme{Sybila} \title[Unfolding for counting experiments]{Unfolding for counting experiments} \author{Marcin Chrz\k{a}szcz$^{1,2}$, Nicola Serra$^{1}$} \institute{$^1$~University of Zurich,\\ $^2$~Institute of Nuclear Physics, Krakow} \date{\today} \begin{document} % --------------------------- SLIDE -------------------------------------------- \frame[plain]{\titlepage} \author{Marcin Chrz\k{a}szcz} % ------------------------------------------------------------------------------ % --------------------------- SLIDE -------------------------------------------- \institute{~(UZH, IFJ)} \section{Introduction} \begin{frame}\frametitle{Reminder 1 - Constructing Matrix unfolding} \begin{itemize} \item We don't know explicate \item I have proven some time ago that the matrix exist \end{itemize} \small{ \begin{equation} \epsilon(\cos \theta_k, \cos \theta_l,\phi) \end{equation} } \begin{itemize} \item I have proven some time ago that the matrix exist \item Now a systemic way to produce it. \item Let's use PHSP MC. \item Moments for PHSP MC are:\\ $v^{T}_{gen}=(2/3 ,0,0,0,0,0,0,0)$ \item After reconstruction we get(full $q^2$ range): $v^{T}_{rec}=( 0.7069,0.0077,-0.00236466,0.0005,0.0007,0.0011,0.0011,-0.0012)$ \end{itemize} \end{frame} \begin{frame}\frametitle{Reminder 2 - Constructing Matrix unfolding} \begin{itemize} \item We got first column of the unfolding matrix $(\dfrac{3}{2} v_{gen})$. \end{itemize} \small{ $ \begin{pmatrix} 1.06 & \cdots & a_{1,8} \\ 0.01157 & \cdots & a_{2,8} \\ -0.003547 & \ddots & \vdots \\ 0.0007841 & \ddots & \vdots \\ 0.001126 & \ddots & \vdots \\ 0.001766 & \ddots & \vdots \\ 0.001664 & \ddots & \vdots \\ -0.001937 & \cdots & a_{8,8} \end{pmatrix}$ } \begin{itemize} \item How about the others? \item We can reweight accordingly to $f_x$. \end{itemize} \end{frame} \begin{frame}\frametitle{Reminder 3 - Constructing Matrix unfolding} \begin{itemize} \item To get $S_3$ each event $i^{th}$ has has weight $f_{S_3}(\cos \theta_{k_i},\cos \theta_{l_i},\phi_i) $ \item One can calculate on MC the reweighed moments in PHPS: \end{itemize} \begin{equation} \int PDF*f_{S_3}=\dfrac{32}{225} \end{equation} \begin{itemize} \item Our base vector now is:$v^{T}_{gen}=(0 ,\frac{32}{225},0,0,0,0,0,0)$ \item So lets see what do we get as reconstructed vector(after multiplying by $\frac{225}{32}$. \small{$v^{T}_{rec}=( 0.042, 1.105,-0.005,0.003,-0.0023,-0.005,-0.005,-0.006)$ } \item Please notice that weights are negative, but this is not a problem for the mean. \item Also we are avoiding the negative PDF problem :) \end{itemize} \end{frame} \begin{frame}\frametitle{Reminder 4 - Constructing Matrix unfolding} \begin{itemize} \item Now the matrix looks like: \end{itemize} \small{ $ \begin{pmatrix} 1.06 & 0.042 & \cdots & a_{1,8} \\ 0.01157 & 1.105 & \cdots & a_{2,8} \\ -0.003547 & -0.005 & \ddots & \vdots \\ 0.0007841 &-0.005 & \ddots & \vdots \\ 0.001126 & 0.003 &\ddots & \vdots \\ 0.001766 & -0.0023 &\ddots & \vdots \\ 0.001664 & -0.005 &\ddots & \vdots \\ -0.001937 & -0.006 &\cdots & a_{8,8} \end{pmatrix}$ } \begin{itemize} \item The others go in the same way. \item Repenting this exercise from $1^{st}$ year algebra we can get the full matrix \end{itemize} \end{frame} \begin{frame}\frametitle{Reminder 5} For now: \begin{itemize} \item We have proven that there has to exists unfolding matrix. \item Shown how to construct transformation matrix: $Gen \to Reco$. \item Inverting it we can have transformation matrix of $Reco \to Gen$. \item For details: \href{https://indico.cern.ch/event/316905/session/1/contribution/18/material/slides/0.pdf}{LINK} \end{itemize} What is missing? \begin{columns} \column{1in} \begin{enumerate} \item ERROR! \end{enumerate} \column{4in} \includegraphics[width=0.8\textwidth]{err.jpg}\\ \end{columns} \end{frame} \begin{frame}\frametitle{How to?} \begin{itemize} \item So lets say that transformation matrix:$Gen \to Reco$ is $\epsilon_{i,j}$. \item Each element has an error:$\delta \epsilon_{i,j}$. \item Then we can calculate the matrix: $\epsilon_{i,j}^{-1}$(assuming it exists). \item The million dollar question is what is the error on inverted matrix? \end{itemize} \end{frame} \begin{frame}\frametitle{Answer to 1M dolar quesion} \only<1>{ \begin{itemize} \item One can toy it. \item But toying is good for kids and Frequentist. \end{itemize} } \only<2>{ \begin{itemize} \item One can toy it. \item But toying is good for kids and Frequentist. \end{itemize} \begin{itemize} \item Solution comes from $\tau$ physics :) \href{http://arxiv.org/abs/hep-ex/9909031}{hep-ex/9909031} \end{itemize} \begin{itemize} \item One can derive(prove in the paper) the general equation: \end{itemize} \begin{equation} \delta \epsilon^{-1}_{\alpha ~ \beta}= [\epsilon^{-1}]^2_{\alpha i}[\delta \epsilon ]^2_{ij} [\epsilon^{-1}]^2_{j \beta} \end{equation} } \end{frame} \begin{frame}\frametitle{Matrix, $1.1-2~GeV$} \tiny{ $ A_{reco\rightarrow gen}=\begin{pmatrix} 0.9519 & -0.02665 & -0.01432 & 0.002356 & 0.02539 & 0.009878 & -0.01551 & -0.01874 \\ -0.006272 & 0.8122 & -0.00351 & -0.00719 & 0.003585 & 6.784e-05 & 0.02445 & 0.008515 \\ -0.005315 & -0.003716 & 1.048 & 0.01242 & 0.01209 & -0.01478 & -0.001956 & 0.01429 \\ 0.003237 & -0.007177 & 0.01533 & 0.9184 & -0.007548 & -0.0009818 & -0.01874 & 0.009407 \\ 0.01002 & 0.004084 & 0.01391 & -0.006509 & 1.194 & -0.006516 & 0.001536 & -0.02882 \\ 0.002695 & -0.001042 & -0.01721 & -0.001842 & -0.005643 & 0.9264 & 0.02106 & 0.006755 \\ -0.004736 & 0.02346 & -0.002335 & -0.01446 & 0.001169 & 0.01697 & 1.072 & -0.003191 \\ -0.004157 & 0.007576 & 0.01377 & 0.008058 & -0.02219 & 0.005354 & -0.0008608 & 0.8304 \end{pmatrix}$ } {~}\\{~}\\{~}\\ \tiny{ $ \delta A_{reco\rightarrow gen}=\begin{pmatrix} 0.005202 & 0.01911 & 0.03258 & 0.02103 & 0.02252 & 0.02145 & 0.03366 & 0.01948 \\ 0.006648 & 0.04654 & 0.03227 & 0.02451 & 0.03602 & 0.02464 & 0.03298 & 0.03397 \\ 0.007557 & 0.03197 & 0.07845 & 0.04272 & 0.04744 & 0.03057 & 0.05698 & 0.03287 \\ 0.007902 & 0.03885 & 0.0678 & 0.04839 & 0.0384 & 0.03464 & 0.04925 & 0.03989 \\ 0.009015 & 0.04122 & 0.06374 & 0.03254 & 0.07349 & 0.03269 & 0.0649 & 0.04202 \\ 0.007939 & 0.0389 & 0.04793 & 0.03433 & 0.03828 & 0.04937 & 0.06985 & 0.04023 \\ 0.007651 & 0.03234 & 0.05611 & 0.03062 & 0.04776 & 0.04388 & 0.08157 & 0.03342 \\ 0.006719 & 0.03345 & 0.03868 & 0.02953 & 0.03633 & 0.03002 & 0.03989 & 0.04827 \end{pmatrix}$ } \end{frame} \begin{frame}\frametitle{What did go wrong?} \begin{itemize} \item The errors are $2-3\%$, which is very worrying. \item WG got very worried what is going on with the errors :( \item Started debugging. \item After sleeping with the problem found a stupid: \end{itemize} \textbf{ for(int i=0;i $<$ entries/10;++i) } \begin{itemize} \item Ok, I am an idiot, and used $10\%$ of statistics. \end{itemize} \end{frame} \begin{frame}\frametitle{What did go wrong 2 ?} \begin{itemize} \item The errors are tricky. When you re-weight you have negative weights. \item So I change the normal error \end{itemize} \begin{equation} \Hat\sigma^2 = \dfrac{\sum w_i}{(\sum w_i)^2 - \sum w_i^2} \sum w_i (x_i - \Hat\mu)^2 \end{equation} \begin{itemize} \item to: \end{itemize} \begin{equation} \Hat\sigma^2 = \dfrac{\sum |w_i| }{(\sum |w_i|)^2 - \sum w_i^2} \sum |w_i| (x_i - \Hat\mu)^2 \end{equation} \begin{itemize} \item And this I am not $100\%$ sure if I is ok =( \end{itemize} \end{frame} \begin{frame}\frametitle{What did go wrong 3 ?} \begin{itemize} \item There is a hack of this method: \item "You can cheat on your gf, you can cheat on tax, but you can't cheat on $\sqrt{n}$ "\footnote{All rights reserved! }. \end{itemize} \begin{center} \includegraphics[width=0.5\textwidth]{Q2_5_6_S5.png}\\ \end{center} \begin{itemize} \item We can use this: \item Divide the MC in 10. Then calculate the variance of each matrix element. And divide/multiply by $\sqrt{10}$ and see if the errors are ok. \end{itemize} \end{frame} \begin{frame}\frametitle{What did go wrong 3 ?} \tiny{ OLD (can be wrong): \\ $ \delta A_{gen\rightarrow reco}=\begin{pmatrix} 0.005477 & 0.02348 & 0.03125 & 0.02305 & 0.01871 & 0.02307 & 0.03124 & 0.02339 \\ 0.008142 & 0.06734 & 0.03621 & 0.03126 & 0.0352 & 0.03131 & 0.03624 & 0.04767 \\ 0.007168 & 0.0359 & 0.06856 & 0.0423 & 0.03619 & 0.02995 & 0.04856 & 0.03585 \\ 0.008573 & 0.04966 & 0.06736 & 0.05471 & 0.03332 & 0.03886 & 0.04784 & 0.04973 \\ 0.007599 & 0.04063 & 0.04926 & 0.02847 & 0.04998 & 0.02841 & 0.04923 & 0.04059 \\ 0.008582 & 0.04977 & 0.04768 & 0.03878 & 0.03323 & 0.05499 & 0.0676 & 0.04974 \\ 0.007136 & 0.03571 & 0.04833 & 0.02987 & 0.036 & 0.04225 & 0.06843 & 0.0358 \\ 0.008162 & 0.04782 & 0.04294 & 0.03731 & 0.03527 & 0.03738 & 0.04306 & 0.06736 \end{pmatrix}$ } \tiny{ New: \\ $ \delta A_{gen\rightarrow reco}=\begin{pmatrix} 0.006659 & 0.0299 & 0.02207 & 0.01802 & 0.02657 & 0.02196 & 0.02851 & 0.02507 \\ 0.00708 & 0.02046 & 0.007998 & 0.0133 & 0.008828 & 0.01236 & 0.01505 & 0.0149\\ 0.008469 & 0.00845 & 0.01806 & 0.01442 & 0.009856 & 0.008895 & 0.01389 & 0.01155\\ 0.008938 & 0.01569 & 0.01798 & 0.01801 & 0.009195 & 0.01097 & 0.01108 & 0.02068\\ 0.007867 & 0.0109 & 0.01248 & 0.0088 & 0.01104 & 0.0114 & 0.01256 & 0.01097\\ 0.008078 & 0.01582 & 0.01117 & 0.01093 & 0.01135 & 0.01215 & 0.02122 & 0.01774 \\ 0.008368 & 0.01521 & 0.01391 & 0.008972 & 0.009797 & 0.01702 & 0.0147 & 0.01086\\ 0.005745 & 0.01561 & 0.0114 & 0.01649 & 0.008631 & 0.01373 & 0.01051 & 0.01792 \end{pmatrix}$ } \end{frame} \begin{frame}\frametitle{Summary} \begin{itemize} \item I really fu.. this thing ... \item No coding after 3 am form now! \end{itemize} \includegraphics[width=0.5\textwidth]{code.png}\\ \end{frame} \end{document}