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finish the discussion
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GL/protokoll/figs/off_60.pdf
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GL/protokoll/figs/off_80.pdf
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@ -461,7 +461,7 @@ Da \(g_1(R_1=\infty)=1\) folgt mit \(R_2=\SI{1}{\meter}\) und \(0\leq g_2\leq 1\
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\end{equation}
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Das ist auch aus dem Stabilit\"atsdiagramm ersichtlich.
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\begin{figure}[H]\centering
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\begin{figure}[b]\centering
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\includegraphics[width=.5\columnwidth]{figs/stabdiag.pdf}
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\caption[Gauss]{Stabilit\"atsdiagramm}
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\label{fig:stabdiag}
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@ -616,13 +616,13 @@ Vernachl\"assigt wurden hier die Ungenaugkeiten, die sich beim
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Einstellen des Leistungsmaximums durch Beamwalken ergeben, da die
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Betrachtungen hier eher qualitativer Natur sind.
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\begin{figure}[h]\centering
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\begin{figure}[b]\centering
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\includegraphics[width=.8\columnwidth]{figs/power-over-l.pdf}
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\caption{Maximale Durchschnittsleistung in Abh\"angigkeit der Resonatorl\"ange }
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\label{fig:power-over-l}
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\end{figure}
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\begin{table}[h]
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\begin{table}[b]
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\centering
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\begin{tabular}{SSS}
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\toprule
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@ -658,13 +658,13 @@ Abweichungsgrenzen hinaus deutet auf untersch\"atzte (systematsiche)
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Faktoren hin.
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\begin{figure}[h]\centering
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\begin{figure}[b]\centering
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\includegraphics[width=.8\columnwidth]{figs/malus.pdf}
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\caption{Maximale Durchschnittsleistung in Abh\"angigkeit des Polarisationswinkels}
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\label{fig:malus}
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\end{figure}
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\begin{table}[H]
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\begin{table}[b]
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\centering
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\begin{tabular}{SSS}
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\toprule
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|
@ -721,13 +721,13 @@ Der theoretische Wert f\"ur den Beamwaist liegt bei
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daf\"ur k\"onnten effekte an der Blende und Abweichung der Geometrie
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durch ungenaue Einstellung der Resonatorspiegel sein.
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\begin{figure}[H]\centering
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\begin{figure}[b]\centering
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\includegraphics[width=.8\columnwidth]{figs/kaustik.pdf}
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\caption{Gemessene und Theoretische Kaustik}
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\label{fig:kaustik}
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\end{figure}
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\begin{table}[h]
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\begin{table}[b]
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\centering
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\begin{tabular}{SSS}
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\toprule
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@ -752,7 +752,7 @@ durch ungenaue Einstellung der Resonatorspiegel sein.
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Das in~\ref{fig:faserspek} geplottete Spektrum zeigt, wie zu erwarten
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war, einen gro\ss{}en Peak bein
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\(\lambda_0=\SI{631.89}{\nano\meter}\). Es sind keine individuellen
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\(\lambda_0=\SI{631.9}{\nano\meter}\). Es sind keine individuellen
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Moden erkennbar. Der Abstand der einzelnen Messpunkte betr\"agt rund
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\(\Delta\lambda=\SI{.5}{\nano\meter}\).
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@ -771,7 +771,7 @@ aus \(L=\SI{80+-.5}{\centi\meter}\) erst in vierter Nachkommastelle):
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\end{equation}
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Somit k\"onnen keine individuellen Moden aufgel\"ost werden.
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\begin{figure}[h]\centering
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\begin{figure}[b]\centering
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\includegraphics[width=.8\columnwidth]{figs/faserspek.pdf}
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\caption{Spektrum des offenen \hne{}s}
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\label{fig:faserspek}
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@ -800,45 +800,57 @@ kann man eine Beziehung zwischen \si{u} und \si{\hertz} herstellen.
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\end{eqnarray}
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Die Aufl\"osung des FPI ist also um gr\"o\ss{}enordnungen besser, als
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die des Faserspektrometers.
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die des Faserspektrometers. Die Unsicherheit der Einheitsumrechnung,
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die sich aus der L\"angenmessung des FPI und dem
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Digitalisierungsungenauigkeit fortpflanzt, ist erstaunlich gering.
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\begin{figure}[h]\centering
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\begin{figure}[b]\centering
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\includegraphics[width=.8\columnwidth]{figs/fsrkalib.pdf}
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\caption{Kalibrierung des FSR, Spektrum des Kommerzielen \hne{}}
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\label{fig:fsrkalib}
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\end{figure}
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Falls eine Gr\"
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Falls eine Gr\"o\ss{}e \(g\) in \si{u} gemessen und dann in \si{\hertz}
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umgerechnet wird, so gilt f\"ur ihre unsicherheit:
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\begin{equation}
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\label{eq:uerr}
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\Delta g = \sqrt{\qty(g\cdot\Delta u)^2 + \qty(\Delta g\cdot u)^2}
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\end{equation}
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Diese Relation wird im Folgenden immer angwandt.
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\subsection{Bestimmung der Finesse}
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\label{sec:bestfinesse}
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Zur bestimmung der Finesse wurde das FWHM der vier Peaks
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in~\ref{fig:fsrkalib} gemittelt.
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\begin{eqnarray}
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\begin{align}
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\label{eq:fwhmlaser}
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\overline{\text{FWHM}} = \SI{4.72\pm .31}{u} = \SI{81\pm
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\overline{\text{FWHM}} =&\; \SI{4.72}{u} = \SI{81\pm
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6}{\mega\hertz} \\
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\Delta\overline{\text{FWHM}} =
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\sigma_{\overline{\text{FWHM}}} =&\; \SI{.31}{u}\\
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\Delta\overline{\text{FWHM}} =&
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\sqrt{\qty(\overline{\text{FWHM}}\cdot\Delta u)^2 +
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\qty(\frac{\sigma_{\overline{\text{FWHM}}}}{\sqrt{4}}\cdot u)^2}
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\end{eqnarray}
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\end{align}
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F\"ur die Finesse gilt nun:
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\begin{eqnarray}
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\begin{align}
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\label{eq:finesselaser}
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\mathfrak{F}=\frac{\text{FSR}}{\text{FWHM}}=\SI{24.6\pm 2.0}{} \\
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\Delta\mathfrak{F} =
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\mathfrak{F} =& \frac{\text{FSR}}{\text{FWHM}}=\SI{24.6\pm 2.0}{} \\
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\Delta\mathfrak{F} =&
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\sqrt{\qty(\frac{\Delta\text{FSR}}{\text{FWHM}})^2 + \qty(\frac{\text{FSR}}{\text{FWHM}^2}\cdot\Delta\text{FWHM})^2}
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\end{eqnarray}
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\end{align}
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Das ist sicherlich kein \"Uberragender Wert (vgl. Anleitung, Anhang),
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aber, wie in~\ref{fig:fsrkalib} zu erkennen, zur Aufl\"osung der
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longitudinalen Moden ausreichend.
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\subsection{Modenstruktur des Kommerziellen Lasers}
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\label{sec:modkomm}
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Nach~\ref{fig:polarisations} haben die beiden erkennbaren Moden des
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kommerziellen Lasers genau ortogonale Polarisation. Ein plot beider
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Spektra in ein Diagramm war leider nicht m\"oglich, da die Daten einer
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@ -864,7 +876,141 @@ gemittelt:
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\end{figure}
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\begin{equation}
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\label{eq:modeabstkom}
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\delta\nu_k=\
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\overline{\delta\nu_k}=\SI{37.6\pm 2.2}{u}=\SI{650\pm 40}{\mega\hertz}
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\end{equation}
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Die ungenauigkeiten kommen hier aus der Statistik der Mittelung.
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Damit kann nun die unbekannte L\"ange des Resonators bestimmt werden.
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\begin{align}
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L_k =& \frac{c}{2\cdot \delta\nu_k} = \SI{23.1\pm 1.6}{\centi\meter}
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\\
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\Delta L_k =& \abs{\frac{c}{2\cdot\delta\nu_k^2}\cdot \Delta\delta\nu_k}
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\end{align}
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Dieses Ergebnis erschein plausibel und die Pr\"azision ist mit den
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vorhergehenden L\"angenmessungen vergleichbar.
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\subsection{Longitudinale Modenstruktur des Offnen \hne{}}
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\label{sec:longoff}
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Die Bestimmung des Modenabstandes verl\"auft analog
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zu~\ref{sec:modkomm} (auch hier wird gemittelt). Da sich der Masstab
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der Zeitachse des Oszilloskops ge\"ander hat, muss die Umrechnung in
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\si{\hertz} wieder analog zu~\ref{sec:kalibzeitausw} kalibriert
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werden.
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Die gemessenen Spektra und Peakpositionen sind in~\ref{fig:off_80_60} dargestellt.
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\begin{figure}[b]\centering
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\includegraphics[width=.5\columnwidth]{figs/off_80.pdf}
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\includegraphics[width=.5\columnwidth]{figs/off_60.pdf}
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\caption{Spektrum des offenen \hne{} bei \(L=\SI{80}{\centi\meter}\)
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und \(L=\SI{60}{\centi\meter}\)}
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\label{fig:off_80_60}
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\end{figure}
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Die pr\"azision ist hier durmh die geringe Anzahl von sichtbaren Moden
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limitiert.
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Die Ungenauigkeit der Messung der Resonatorl\"ange wurde wiered auf
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\SI{.5}{\centi\meter} gesch\"atzt.
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Mit~\ref{eq:longmodes} kann aus der Resonatorl\"ange der Mberechnet
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werden. Man erh\"alt nun f\"ur die Modenabst\"ande:
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\begin{table}[H]
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\centering
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\begin{tabular}{SSS}
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\toprule
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{\(L\) [\si{\centi\meter}]} & {\(\delta\nu\) Theorie [\si{\mega\hertz}]} & {\(\delta\nu\) Experimentell [\si{\mega\hertz}]}\\
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\midrule
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80 & 187.4\pm 1.2 & 201\pm 14 \\
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60 & 249.8\pm 2.1 & 279\pm 11 \\
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\bottomrule
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\end{tabular}
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\caption{Modenabs\"ande am Offenen \hne{}}
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\label{tab:kaustik}
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\end{table}
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Es ergibt sich also f\"ur \(L=\SI{60}{\centi\meter}\) ergibt sich also
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\"ubereinstimmung innerhalb der Fehlergrenzen. F\"ur
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\(L=\SI{60}{\centi\meter}\) ist die differenz gr\"o\ss{}er als die
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Messungenaugkeiten. Dieser Umstand k\"onnte eventuell auf die geringe
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Anzahl von Peaks \"uber die gemittelt wird zur\"uzufu\"hern sein. Da
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somit die Statistik mangelhaft wird, k\"onnten vernachl\"assigte
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systematische Abweichungen zum Tragen kommen (die Messunsicherheiten
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wurden untersch\"atzt).
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\subsection{Betrachtung der Linienverbreiterung}
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\label{sec:linver}
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Die geringe Anzahl sichtbaren Moden macht es schwierig, qualifizierte
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Aussagen \"uber die Einh\"ullende zu treffen. Die geringe Anzahl der
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sichtbaren Moden l\"asst auf eine hohe Verlustgrenze
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schlie\ss{}en. Eventuel wurden auch die zum Ausblenden der
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ungew\"unschten Transversalmoden verwendete Blende zusehr zugedreht.
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Die Temperatur in der Laser R\"ohre sollte die Umgebungstemperatur
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\(\approx \SI{300}{\kelvin}\) \"ubersteigen. Wie in der Anleitung und
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in \todo{Buch Zitieren} dargestellt, sollte bei solchen Temperaturen
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der Hauptanteil der Linienverbreiterung durch die Inhomogene
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Dopplerverbreiterung zustandekommen. Die einh\"ullende des
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Modenspektrums sollte also einer Gausskurve Gleichen, da die
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Intensit\"aten der einzelnen Moden zum Profil der Dopplerverbreiterung
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proportional sind (Gau\ss{}kurve).
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Um einen sch\"atzer f\"ur die Linienverbreiterung zu erhalten wurde
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eine Gaussfunktion \"uber die drei bei \(L=\SI{80}{\centi\meter}\)
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sichtbaren Peaks mit abgezogener Baseline gefittet
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(siehe~\ref{fig:fit_einh}). Als freie Parameter wurden die
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Standartabweichung \(\sigma\) und die H\"ohe gew\"ahlt. Der
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Mittelwert wurde fest auf den h\"ochsten Peak gelegt, da die
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Verst\"arkung im Zentrum des Verbreiterungsprofiels am gr\"o\ss{}ten
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ist.
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\begin{figure}[b]\centering
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\includegraphics[width=.8\columnwidth]{figs/verbr_fit.pdf}
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\caption{Einh\"ullende der Intesit\"aten der Longitudinalen Moden
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des Offenen \hne{}}
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\label{fig:fit_einh}
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\end{figure}
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F\"ur die Standardabweichung und Breite ergibt sich nun:
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\begin{align}
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\sigma =& \SI{53\pm 20}{u} = \SI{340\pm 130}{\mega\hertz} \\
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\Delta\nu =& 2\sqrt{2\ln{2}}\cdot\sigma = \SI{800\pm 300}{\mega\hertz}
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\end{align}
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Die Abweichung von \(\sigma\) ergibt sich nicht aus dem fit Residuum,
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sondern wurde gesch\"atzt. Die erhaltene Lininenverbreiterung
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\(\Delta\nu\) ist ungef\"ahr halb so gro\ss{} wie der in der
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Literatur f\"ur \hne{} angegebene \todo{Zitat aus dem Laserbuch!}.
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Dementsprechend erh\"alt man mit~\ref{eq:doppler} (angepasst f\"ur
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\(\sigma\) anstatt der Halbwertsbreite), wobei
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\(m=\SI{3.35092e-26}{\kg}\) und \(\nu_0=\SI{473.755}{\tera\hertz}\)
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\todo{Zitat Wikipedia qelle suchen}
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\begin{align}
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\label{eq:temp}
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T = \qty(\frac{\sigma\cdot c}{\nu_0})^2\cdot \frac{m}{k_B}=\SI{110\pm 90}{\kelvin}
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\end{align}
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Das ist also selbst mit bei Auss\"opfung der Unsicherheitsgernzen kein
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sinnvolles Ergebnis, da die Temperatur sehr weit unter dem
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Gefrierpunkt und allemal unter der Zimmertemperatur
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liegt. Da~\ref{eq:temp} Quadratisch in \(\sigma\) ist, bewirkt eine
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verdopplung der Breite eine Vervierfachung der erhaltenen
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Temperatur. Die zu geringe Linienbreite verf\"alscht die errechnete
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Temperatur also enorm. F\"ur ein plausibles Ergebnis w\"ahre fast die
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Doppelte breite \(\sigma\) notwendig. Soetwas l\"asst sich nicht als
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Unsicherheit behandeln, da die Fehlergrenzen dann negative Temperaturen
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(in \si{\kelvin}) umfassen.
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Drei sichtbare Moden lassen also keine vern\"unftige
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Temperaturabsch\"atzung zu.
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\end{document}
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|
|
|
@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 54,
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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|
@ -18,7 +18,8 @@
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"import numpy as np\n",
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"import pandas as pd\n",
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"from scipy.signal import find_peaks, find_peaks_cwt\n",
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"from SecondaryValue import SecondaryValue"
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"from SecondaryValue import SecondaryValue\n",
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"from scipy.optimize import curve_fit"
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]
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},
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||||
{
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||||
|
@ -185,7 +186,7 @@
|
|||
},
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||||
{
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||||
"cell_type": "code",
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"execution_count": 10,
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"execution_count": 57,
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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||||
|
@ -197,9 +198,8 @@
|
|||
},
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"outputs": [],
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"source": [
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"def calibrate_unit(ydata, thresh):\n",
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"def plot_peaks(ydata, peaks):\n",
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" xdata = np.arange(0, len(ydata))\n",
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" peaks, peak_info = find_peaks(ydata, height=thresh, width=1, rel_height=.5)\n",
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"\n",
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" plt.clf()\n",
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" plt.plot(xdata, ydata, label='Spektrum')\n",
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|
@ -211,6 +211,9 @@
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" plt.autoscale(enable=True, axis='x', tight=True)\n",
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" plt.show()\n",
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" \n",
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"def calibrate_unit(ydata, thresh):\n",
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" peaks, peak_info = find_peaks(ydata, height=thresh, width=1, rel_height=.5)\n",
|
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" plot_peaks(ydata, peaks)\n",
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" return SecondaryValue('f/(x2-x1)')(f=fsr, x2=(peaks[1], 1), x1=(peaks[0], 1))\n",
|
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"\n",
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"\n"
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||||
|
@ -338,7 +341,7 @@
|
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},
|
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{
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": 16,
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"metadata": {
|
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"autoscroll": false,
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"collapsed": false,
|
||||
|
@ -355,7 +358,7 @@
|
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},
|
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{
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": 17,
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"metadata": {
|
||||
"autoscroll": false,
|
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"collapsed": false,
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|
@ -372,7 +375,7 @@
|
|||
"(24.581172150793947, 1.9961301696025398)"
|
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]
|
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},
|
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"execution_count": 20,
|
||||
"execution_count": 17,
|
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"metadata": {},
|
||||
"output_type": "execute_result"
|
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}
|
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|
@ -383,7 +386,7 @@
|
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},
|
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{
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"cell_type": "code",
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"execution_count": 21,
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||||
"execution_count": 18,
|
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"metadata": {
|
||||
"autoscroll": false,
|
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"collapsed": false,
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|
@ -404,7 +407,7 @@
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},
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{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"execution_count": 19,
|
||||
"metadata": {
|
||||
"autoscroll": false,
|
||||
"collapsed": false,
|
||||
|
@ -429,7 +432,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
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"execution_count": 23,
|
||||
"execution_count": 33,
|
||||
"metadata": {
|
||||
"autoscroll": false,
|
||||
"collapsed": false,
|
||||
|
@ -448,7 +451,35 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"execution_count": 34,
|
||||
"metadata": {
|
||||
"autoscroll": false,
|
||||
"collapsed": false,
|
||||
"ein.hycell": false,
|
||||
"ein.tags": "worksheet-0",
|
||||
"slideshow": {
|
||||
"slide_type": "-"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(37.6, 2.2199099080818567)"
|
||||
]
|
||||
},
|
||||
"execution_count": 34,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"dist"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
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"metadata": {
|
||||
"autoscroll": false,
|
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"collapsed": false,
|
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|
@ -465,7 +496,7 @@
|
|||
"(647828551.724138, 44719227.898102246)"
|
||||
]
|
||||
},
|
||||
"execution_count": 24,
|
||||
"execution_count": 35,
|
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"metadata": {},
|
||||
"output_type": "execute_result"
|
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}
|
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|
@ -476,7 +507,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"execution_count": 36,
|
||||
"metadata": {
|
||||
"autoscroll": false,
|
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"collapsed": false,
|
||||
|
@ -493,7 +524,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"execution_count": 37,
|
||||
"metadata": {
|
||||
"autoscroll": false,
|
||||
"collapsed": false,
|
||||
|
@ -510,7 +541,7 @@
|
|||
"(0.23138256040284816, 0.015972203483102267)"
|
||||
]
|
||||
},
|
||||
"execution_count": 26,
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
@ -549,7 +580,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 54,
|
||||
"execution_count": 38,
|
||||
"metadata": {
|
||||
"autoscroll": false,
|
||||
"collapsed": false,
|
||||
|
@ -566,7 +597,7 @@
|
|||
"(37.6, 2.2199099080818567)"
|
||||
]
|
||||
},
|
||||
"execution_count": 54,
|
||||
"execution_count": 38,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
@ -577,7 +608,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"execution_count": 39,
|
||||
"metadata": {
|
||||
"autoscroll": false,
|
||||
"collapsed": false,
|
||||
|
@ -594,7 +625,7 @@
|
|||
"array([ 88, 204, 313, 443, 574])"
|
||||
]
|
||||
},
|
||||
"execution_count": 56,
|
||||
"execution_count": 39,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
@ -605,7 +636,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 178,
|
||||
"execution_count": 59,
|
||||
"metadata": {
|
||||
"autoscroll": false,
|
||||
"collapsed": false,
|
||||
|
@ -624,26 +655,16 @@
|
|||
" peak_batches = np.split(all_peaks, num_batches)\n",
|
||||
"\n",
|
||||
" \n",
|
||||
" xdata = np.arange(0, len(ydata))\n",
|
||||
" plt.clf()\n",
|
||||
" plt.plot(xdata, ydata, label='Spektrum')\n",
|
||||
" plt.plot(xdata[all_peaks], ydata[all_peaks], \"x\",\n",
|
||||
" color='red', label='Peaks')\n",
|
||||
" plt.xlabel('Zeit [u]')\n",
|
||||
" plt.ylabel('Amplitude [mV]')\n",
|
||||
" plt.legend()\n",
|
||||
" plt.autoscale(enable=True, axis='x', tight=True)\n",
|
||||
" plt.show()\n",
|
||||
" \n",
|
||||
" plot_peaks(ydata, all_peaks)\n",
|
||||
" dists = np.concatenate([d[1:] - d[:-1] for d in peak_batches])\n",
|
||||
" dist = dists.mean(), dists.std()/np.sqrt(len(dists))\n",
|
||||
" return SecondaryValue('u*d')(u=new_unit, d=dist)\n",
|
||||
"mode_dist = SecondaryValue('c/(2*L)', defaults=dict(c=29979245800)) "
|
||||
"mode_dist = SecondaryValue('c/(2*L)', defaults=dict(c=29979245800))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 186,
|
||||
"execution_count": 60,
|
||||
"metadata": {
|
||||
"autoscroll": false,
|
||||
"collapsed": false,
|
||||
|
@ -662,7 +683,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 187,
|
||||
"execution_count": 51,
|
||||
"metadata": {
|
||||
"autoscroll": false,
|
||||
"collapsed": false,
|
||||
|
@ -676,10 +697,10 @@
|
|||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(187973102.89389068, 12894642.34716403)"
|
||||
"(200825964.6302251, 14256332.17614453)"
|
||||
]
|
||||
},
|
||||
"execution_count": 187,
|
||||
"execution_count": 51,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
@ -690,7 +711,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 163,
|
||||
"execution_count": 52,
|
||||
"metadata": {
|
||||
"autoscroll": false,
|
||||
"collapsed": false,
|
||||
|
@ -707,7 +728,7 @@
|
|||
"(187370286.25, 1171064.2890625)"
|
||||
]
|
||||
},
|
||||
"execution_count": 163,
|
||||
"execution_count": 52,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
@ -718,7 +739,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 190,
|
||||
"execution_count": 53,
|
||||
"metadata": {
|
||||
"autoscroll": false,
|
||||
"collapsed": false,
|
||||
|
@ -732,10 +753,10 @@
|
|||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(278877209.3023256, 10543335.772201253)"
|
||||
"(278877209.3023256, 10568707.957255056)"
|
||||
]
|
||||
},
|
||||
"execution_count": 190,
|
||||
"execution_count": 53,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
@ -749,7 +770,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 191,
|
||||
"execution_count": 49,
|
||||
"metadata": {
|
||||
"autoscroll": false,
|
||||
"collapsed": false,
|
||||
|
@ -766,7 +787,7 @@
|
|||
"(249827048.33333334, 2081892.0694444445)"
|
||||
]
|
||||
},
|
||||
"execution_count": 191,
|
||||
"execution_count": 49,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
@ -774,12 +795,162 @@
|
|||
"source": [
|
||||
"mode_dist(L=(60,.5))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 127,
|
||||
"metadata": {
|
||||
"autoscroll": false,
|
||||
"collapsed": false,
|
||||
"ein.hycell": false,
|
||||
"ein.tags": "worksheet-0",
|
||||
"slideshow": {
|
||||
"slide_type": "-"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"doppler_data = off_1[50:300]\n",
|
||||
"doppler_peaks, _ = find_peaks(doppler_data, distance=10, height=50)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 144,
|
||||
"metadata": {
|
||||
"autoscroll": false,
|
||||
"collapsed": false,
|
||||
"ein.hycell": false,
|
||||
"ein.tags": "worksheet-0",
|
||||
"slideshow": {
|
||||
"slide_type": "-"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"m = doppler_peaks[0]\n",
|
||||
"o = np.median(doppler_data)\n",
|
||||
"def gauss(x, s, A):\n",
|
||||
" return A*np.exp(-(x-m)**2/(2*s**2)) + o\n",
|
||||
"\n",
|
||||
"doppler, ddoppler = curve_fit(gauss, doppler_peaks, doppler_data[doppler_peaks],\n",
|
||||
" p0=(20, doppler_data[doppler_peaks[0]]))\n",
|
||||
"ddoppler = np.sqrt(np.diag(ddoppler))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 173,
|
||||
"metadata": {
|
||||
"autoscroll": false,
|
||||
"collapsed": false,
|
||||
"ein.hycell": false,
|
||||
"ein.tags": "worksheet-0",
|
||||
"slideshow": {
|
||||
"slide_type": "-"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plot_peaks(doppler_data, doppler_peaks)\n",
|
||||
"xs = np.linspace(0, 250, 1000)\n",
|
||||
"plt.plot(xs, gauss(xs, *doppler), label='Einhuellende')\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()\n",
|
||||
"\n",
|
||||
"sigma = SecondaryValue('s*u')(s=(doppler[0], 40), u=unit_1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 174,
|
||||
"metadata": {
|
||||
"autoscroll": false,
|
||||
"collapsed": false,
|
||||
"ein.hycell": false,
|
||||
"ein.tags": "worksheet-0",
|
||||
"slideshow": {
|
||||
"slide_type": "-"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(113.22422437226905, 170.7171426794478)"
|
||||
]
|
||||
},
|
||||
"execution_count": 174,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"T = SecondaryValue('(s*c/n0)^2*m/k')(c=299792458, s=sigma, m=3.35092e-26, n0=4.73755e14, k=1.380649e-23)\n",
|
||||
"T"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 172,
|
||||
"metadata": {
|
||||
"autoscroll": false,
|
||||
"collapsed": false,
|
||||
"ein.hycell": false,
|
||||
"ein.tags": "worksheet-0",
|
||||
"slideshow": {
|
||||
"slide_type": "-"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"1346844852.1465542"
|
||||
]
|
||||
},
|
||||
"execution_count": 172,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sigma[0]*2*np.sqrt(2*np.log(2))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 158,
|
||||
"metadata": {
|
||||
"autoscroll": false,
|
||||
"collapsed": false,
|
||||
"ein.hycell": false,
|
||||
"ein.tags": "worksheet-0",
|
||||
"slideshow": {
|
||||
"slide_type": "-"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(array([53.11198736, 47.34934225]),\n array([8.08987576, 4.31644846]),\n (341320515.0376786, 129049367.49361125))"
|
||||
]
|
||||
},
|
||||
"execution_count": 158,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"doppler, ddoppler, sigma"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"argv": [
|
||||
"/usr/bin/python3",
|
||||
"python",
|
||||
"-m",
|
||||
"ipykernel_launcher",
|
||||
"-f",
|
||||
|
|
Loading…
Add table
Reference in a new issue