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5000i,500n,10000p,200000b,80000s -Output written on mchrzasz.pdf (17 pages). +Output written on mchrzasz.pdf (25 pages). diff --git a/ReInterpretation/2019/mchrzasz.nav b/ReInterpretation/2019/mchrzasz.nav index 7d97f76..35cff68 100644 --- a/ReInterpretation/2019/mchrzasz.nav +++ b/ReInterpretation/2019/mchrzasz.nav @@ -26,8 +26,24 @@ \headcommand {\beamer@framepages {16}{16}} \headcommand {\slideentry {0}{0}{13}{17/17}{}{0}} \headcommand {\beamer@framepages {17}{17}} -\headcommand {\beamer@partpages {1}{17}} -\headcommand {\beamer@subsectionpages {1}{17}} -\headcommand {\beamer@sectionpages {1}{17}} -\headcommand {\beamer@documentpages {17}} -\headcommand {\def \inserttotalframenumber {12}} +\headcommand {\slideentry {0}{0}{14}{18/18}{}{0}} +\headcommand {\beamer@framepages {18}{18}} +\headcommand {\slideentry {0}{0}{15}{19/19}{}{0}} +\headcommand {\beamer@framepages {19}{19}} +\headcommand {\slideentry {0}{0}{16}{20/20}{}{0}} +\headcommand {\beamer@framepages {20}{20}} +\headcommand 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b296703..ab58085 100644 --- a/ReInterpretation/2019/mchrzasz.snm +++ b/ReInterpretation/2019/mchrzasz.snm @@ -1 +1,2 @@ \beamer@slide {eq:wilks}{15} +\beamer@slide {eq:chi2ndim}{18} diff --git a/ReInterpretation/2019/mchrzasz.synctex.gz b/ReInterpretation/2019/mchrzasz.synctex.gz index 11564e5..7d2a7d2 100644 --- a/ReInterpretation/2019/mchrzasz.synctex.gz +++ b/ReInterpretation/2019/mchrzasz.synctex.gz Binary files differ diff --git a/ReInterpretation/2019/mchrzasz.tex b/ReInterpretation/2019/mchrzasz.tex index 487b0b1..5153d3d 100644 --- a/ReInterpretation/2019/mchrzasz.tex +++ b/ReInterpretation/2019/mchrzasz.tex @@ -466,13 +466,175 @@ \begin{frame}\frametitle{Single measurement, symmetric error, \texttt{HL\_Gaussian}} +\ARROW Well this is as simple as: +\begin{center} +\includegraphics[width=0.6\textwidth]{images/yaml4.png} +\end{center} +\ARROW The $\chi^2$ is simple: +\begin{equation} +\chi^2 = \frac{(x_{obs}-x)^{2}}{ \sigma_{stat}^{2}+ \sigma_{syst}^{2} }, +\end{equation} +\ARROW Wilks theorem can be used to translate to (log-)likelihood. \end{frame} +\begin{frame}\frametitle{Single measurement, symmetric error, \texttt{HL\_Gaussian}} + +\ARROW Well this is as simple as: +\begin{center} +\includegraphics[width=0.6\textwidth]{images/yaml4.png} +\end{center} +\ARROW The $\chi^2$ is simple: +\begin{equation} +\chi^2 = \frac{(x_{obs}-x)^{2}}{ \sigma_{stat}^{2}+ \sigma_{syst}^{2} }, +\end{equation} + +\ARROW Wilks theorem can be used to translate to (log-)likelihood. + + +\end{frame} + + + + +\begin{frame}\frametitle{Multiple measurement, symmetric error, \texttt{HL\_nDimGaussian}} + +\ARROW You need to pass two arguments: +\begin{center} +\includegraphics[width=0.6\textwidth]{images/yaml5.png} +\end{center} +\ARROW From this one constructs the covariance matrix, and evaluates the $\chi^2$: +\begin{align} +\chi^2 = V^{T} {\rm Cov}^{-1} V,\label{eq:chi2ndim} +\end{align} + + +\end{frame} + + +\begin{frame}\frametitle{Measurement, asymmetric error, \texttt{HL\_BifurGaussian}, \texttt{HL\_ndimBifurGaussian}} + +\ARROW You need to pass two arguments: +\begin{center} +\includegraphics[width=0.6\textwidth]{images/yaml6.png} +\end{center} + +\ARROW We choose to interpret this as Bifurcated Gaussian: + + \begin{align} +{\rm Cov}_{i,j}= +\begin{cases} +{\rm Corr}_{i,j}~\sigma^{i}_+ \sigma^{j}_+, & \text{if } x^i \geq x^i_{obs} \text{ and } x^j \geq x^j_{obs} \\ +{\rm Corr}_{i,j}~\sigma^{i}_+ \sigma^{j}_-, & \text{if } x^i \geq x^i_{obs} \text{ and } x^j < x^j_{obs} \\ +{\rm Corr}_{i,j}~\sigma^{i}_- \sigma^{j}_+, & \text{if } x^i < x^i_{obs} \text{ and } x^j \geq x^j_{obs} \\ +{\rm Corr}_{i,j}~\sigma^{i}_- \sigma^{j}_-, & \text{if } x^i < x^i_{obs} \text{ and } x^j < x^j_{obs} \\ +\end{cases} +\end{align} + + +\end{frame} + + + + +\begin{frame}\frametitle{Likelihoods, \texttt{HL\_ProfLikelihood}, \texttt{HL\_nDimLikelihood}} + +\ARROW Here we add just the location of \texttt{ROOT} object.\\ +\begin{columns} +\column{0.7\textwidth} + \begin{center} +\includegraphics[width=0.9\textwidth]{images/yaml7.png}\\ +\includegraphics[width=0.9\textwidth]{images/yaml8.png} +\end{center} + +\ARROW This is the best way to publish results!!!\\ +\ARROW The problem is in what way one should publish the higher dim likelihoods? + + +\column{0.3\textwidth} +\includegraphics[angle=-90,width=0.95\textwidth]{images/Fig2-S.pdf}\\ +\includegraphics[angle=-90,width=0.95\textwidth]{images/Fig21.pdf} +\end{columns} + + + + + +\end{frame} + + + +\begin{frame}\frametitle{Publishing data} + + \includegraphics[width=0.95\textwidth]{images/patrick.png} + + + + Stolen from P. Owen +\end{frame} + + + +\begin{frame}\frametitle{Publishing data \texttt{HL\_ExpData}} + +\ARROW The \texttt{YAML} entry: + + \includegraphics[width=0.5\textwidth]{images/yaml9.png}\\ + \ARROW Set the PDF you want to fit:\\ \texttt{double (*fun)(vector par , vector point)}\\ + \ARROW The program will evaluate the (log-)likelihood on the whole dataset for given parameters.\\ + \ARROW You only need a scanning tools and you are done. + + +\end{frame} + + +\begin{frame}\frametitle{Useful functions} + + + +\ARROW Search for measurement you need: +\begin{center} +\includegraphics[width=0.9\textwidth]{images/example.png} +\end{center} + + + +\ARROW Create citation file: +\begin{center} +\includegraphics[width=0.9\textwidth]{images/example2.png} +\end{center} + + +\end{frame} + + + + + + + + +\begin{frame}\frametitle{Check it out} + +\ARROW The HEPLike code:\\ +\url{https://github.com/mchrzasz/HEPLike} + +\ARROW The HEPLike database:\\ +\url{https://github.com/mchrzasz/HEPLikeData} + +\begin{alertblock}{} +Don't be shy! Give it a spin. Feedback is welcomed. +\end{alertblock} + + + +\end{frame} + + \backupbegin \begin{frame}\frametitle{Backup}