4 files changed, 27 insertions, 27 deletions

diff --git a/PEDOT_PSS/chapter.tex b/PEDOT_PSS/chapter.tex
index a0ef8e3..24702d1 100644
--- a/PEDOT_PSS/chapter.tex
+++ b/PEDOT_PSS/chapter.tex
@@ -183,8 +183,6 @@ that the two conjugate peak shifts (3PE and 3PE*) agree. \cite{WeinerAM1985a}  %
 
 We found that the 3PEPS traces agree best when the data in \autoref{pps:fig:raw} is offset by
 19 fs in $\tau_{22^\prime}$ and 4 fs in $\tau_{21}$.  %
-\autoref{pps:fig:processed} shows the 3PEPS traces after correcting for the zero delay
-value.  %
 The entire 3PEPS trace ($\tau$ vs $T$) is shown for regions \RomanNumeral{1}, \RomanNumeral{3}
 (purple and light green traces) and \RomanNumeral{5}, \RomanNumeral{6} (yellow and light blue
 traces) for the $\vec{k_1} - \vec{k_2} + \vec{k_{2^\prime}}$ and $\vec{k_1} + \vec{k_2} -
diff --git a/mixed_domain/chapter.tex b/mixed_domain/chapter.tex
index 60910c2..75e60ea 100644
--- a/mixed_domain/chapter.tex
+++ b/mixed_domain/chapter.tex
@@ -164,7 +164,7 @@ the temporal and spectral widths of the excitation pulses.  %
 For simplicity, we will ignore population relaxation effects:  $\Gamma_{11}=\Gamma_{00}=0$.  %

 

 The electric field pulses, $\left\{E_l \right\}$, are given by:

-\begin{equation}\label{eq:E_l}

+\begin{equation} \label{mix:eqn:E_l}

 E_l(t; \omega_l, \tau_l, \vec{k}_l \cdot z) = \frac{1}{2}\left[c_l(t-\tau_l)e^{i\vec{k}_l\cdot z}e^{-i\omega_l(t-\tau_l)} + c.c. \right],

 \end{equation}

 where $\omega_l$ is the field carrier frequency, $\vec{k}_l$ is the wavevector, $\tau_l$ is the

@@ -180,7 +180,7 @@ spectral FWHM (intensity scale).  %
 

 

 The Liouville-von Neumann Equation propagates the density matrix, $\bm{\rho}$:

-\begin{equation}\label{eq:LVN}

+\begin{equation} \label{mix:eqn:LVN}

 \frac{d\bm{\rho}}{dt} = -\frac{i}{\hbar}\left[\bm{H_0} + \bm{\mu}\cdot \sum_{l=1,2,2^\prime} E_l(t), \bm{{\rho}}\right] + \bm{\Gamma \rho}.

 \end{equation}

 Here $\bm{H_0}$ is the time-independent Hamiltonian, $\bm{\mu}$ is the dipole superoperator, and

@@ -202,7 +202,7 @@ $\rho_{ij}$ from $\rho_{ik}$ or $\rho_{kj}$.  %
 For brevity, we use $\hbar=1$ and abbreviate the initial and final density matrix elements as

 $\rho_i$ and $\rho_f$, respectively.  %

 Using the natural frequency rotating frame, $\tilde{\rho}_{ij}=\rho_{ij} e^{-i\omega_{ij}t}$, the formation of $\rho_f$ using pulse $x$ is written as

-\begin{equation}\label{eq:rho_f}

+\begin{equation} \label{mix:eqn:rho_f}

 \begin{split}

 \frac{d\tilde{\rho}_f}{dt} =& -\Gamma_f\tilde{\rho}_f \\ 

 &+ \frac{i}{2} \lambda_f \mu_f c_x(t-\tau_x)e^{i\kappa_f\left(\vec{k}_x\cdot z + \omega_x \tau_x \right)}e^{i\kappa_f\Omega_{fx}t}\tilde{\rho}_i(t),

@@ -223,7 +223,7 @@ In the following equations we neglect spatial dependence ($z=0$).  %
 It provides a general expression for arbitrary values of the dephasing rate and excitation pulse

 bandwidth.  %

 The integral solution is

-\begin{equation}\label{eq:rho_f_int}

+\begin{equation} \label{mix:eqn:rho_f_int}

 \begin{split}

 \tilde{\rho}_f(t) =& \frac{i}{2}\lambda_f \mu_f e^{i\kappa_f \omega_x \tau_x} e^{i\kappa_f \Omega_{fx} t} \\

 &\times \int_{-\infty}^{\infty} c_x(t-u-\tau_x)\tilde{\rho}_i(t-u)\Theta(u) \\

@@ -236,8 +236,8 @@ where $\Theta$ is the Heaviside step function.  %
 \left|\Gamma_f + i \kappa_f \Omega_{fx}\right| \ll 1$.  %

 Both limits are important for understanding the multidimensional line shape changes discussed in

 this paper.  %

-The steady state and impulsive limits of Equation propagates \auotoref{mix:eqn:rho_f_int} are

-discussed in TODO

+%The steady state and impulsive limits of Equation propagates \auotoref{mix:eqn:rho_f_int} are

+% discussed in TODO

 % Appendix \ref{sec:cw_imp}.  %

 

 \begin{figure}

@@ -289,12 +289,12 @@ $2^\prime$ notation for the laser pulses with pathway $V\gamma$.
 

 The electric field emitted from a Liouville pathway is proportional to the polarization created by

 the third-order coherence:  %

-\begin{equation}\label{mix:eqn:E_L}

+\begin{equation} \label{mix:eqn:E_L}

 E_L(t) = i \mu_{4}\rho_{3}(t).

 \end{equation}

 \autoref{mix:eqn:E_L} assumes perfect phase-matching and no pulse distortions through propagation.

 \autoref{mix:eqn:rho_f_int} shows that the output field for this Liouville pathway is

-	\begin{gather}\label{mix:eqn:E_L_full}

+	\begin{gather} \label{mix:eqn:E_L_full}

 	\begin{split}

 	E_L(t) =& \frac{i}{8}\lambda_1\lambda_2\lambda_3\mu_1\mu_2\mu_3\mu_4 

 	e^{i\left( \kappa_1\omega_x\tau_x + \kappa_2\omega_y\tau_y + \kappa_3\omega_z\tau_z \right)} 

@@ -380,7 +380,7 @@ Our simulations were done using the open-source SciPy library. \cite{OliphantTra
 

 The changes in the spectral line shapes described in this work are best understood by examining the

 driven/continuous wave (CW) and impulsive limits of \autoref{mix:eqn:rho_f_int} and

-\ref{eq:E_L_full}.  %

+\ref{mix:eqn:E_L_full}.  %

 The driven limit is achieved when pulse durations are much longer than the response function

 dynamics: $\Delta_t \left|\Gamma_f + i \kappa_f \Omega_{fx}\right| \gg 1$.  %

 In this limit, the system will adopt a steady state over excitation:  $d\rho / dt \approx 0$.  %

@@ -493,7 +493,7 @@ The build-up limit approximates well when pulses are near-resonant and arrive to
 build-up behavior is emphasized).  %

 The driven limit holds for large detunings, regardless of delay.  %

 

-\subsection{Convolution Technique for Inhomogeneous Broadening} \label{mix:sec:mixed_convolution}

+\subsection{Convolution technique for inhomogeneous broadening} \label{mix:sec:convolution}  % ----

 

 \begin{figure}

 	\includegraphics[width=\linewidth]{mixed_domain/convolve}

@@ -712,7 +712,7 @@ $c_x$, which in our case gives
 \mathcal{F}\left\{ c_x \right\}\left( \omega \right) = \frac{1}{\sqrt{2\pi}} e^{-\frac{(\Delta_t\omega)^2}{4\ln 2}}.

 \end{equation}

 For squared-law detection of $\rho_f$, the importance of the tracking monochromator is highlighted

-by two limits of \autoref{eq:rho_f_int_freq}:

+by two limits of \autoref{mix:eqn:rho_f_int_freq}:

 \begin{itemize}

 	\item When the transient is not frequency resolved, $\text{sig} \approx \int{\left|

         \tilde{\rho}_f(\omega) \right|^2 d\omega}$ and the measured line shape will be the

@@ -977,7 +977,7 @@ The colored histogram bars and line shape contours correspond to different value
 dephasing rate, $\Gamma_{10}\Delta_t$.  %

 The contour is the half-maximum of the line shape.  %

 The dependence of the line shape amplitude on $\tau_{21}$ can be inferred from

-\autoref{fig:delay_purity}.  %

+\autoref{mix:fig:delay_purity}.  %

 

 The qualitative trend, as $\tau_{21}$ goes from positive to negative delays, is a change from

 diagonal/compressed line shapes to much broader resonances with no correlation ($\omega_1$ and

@@ -1006,7 +1006,7 @@ There are differences in the line shapes for the different values of the relativ
 $\Gamma_{10}\Delta_t$.  %

 The spectral correlation at $\tau_{21}/\Delta_t=2$ decreases in strength as $\Gamma_{10}\Delta_t$

 decreases.  %

-As we illustrated in \autoref{fig:pw1}, this spectral correlation is a signature of driven signal

+As we illustrated in \autoref{mix:fig:pw1}, this spectral correlation is a signature of driven signal

 from temporal overlap of $E_1$ and $E_2$; the loss of spectral correlation reflects the increased

 prominence of FID in the first coherence as the field-matter interactions become more impulsive.  %

 This increased prominence of FID also reflects an increase in signal strength, as shown by

@@ -1141,7 +1141,7 @@ time-ordering III is decoupled by detuning.  %
 

 In frequency space, spectral elongation along the diagonal is the signature of inhomogeneous

 broadening.  %

-\autoref{fig:inhom_2d_spectra} shows the line shape changes of a Gaussian inhomogeneous

+\autoref{mix:fig:inhom_2d_spectra} shows the line shape changes of a Gaussian inhomogeneous

 distribution.  %

 All systems are broadened by a distribution proportional to their dephasing bandwidth.  %

 As expected, the sequence again shows a gradual broadening along the $\omega_1$ axis, with a strong

@@ -1171,7 +1171,7 @@ For any delay coordinate, one can develop qualitative line shape expectations by
 \end{enumerate}

 \autoref{mix:fig:fid_dpr} illustrates principles 1 and 2 and \autoref{mix:fig:fid_detuning}

 illustrates principle 2 and 3.  %

-\autoref{fig:pw1} provides a detailed example of the relationship between these principles and the

+\autoref{mix:fig:pw1} provides a detailed example of the relationship between these principles and the

 multidimensional line shape changes for different delay times.  %

 

 The principles presented above apply to a single pathway.  %

@@ -1182,7 +1182,8 @@ shape.  %
 The relative weight of each pathway to the interference can be approximated by the extent of pulse

 overlap.  %

 The pathway weights exchange when scanning across pulse overlap, which creates the dramatic line

-shape changes observed in Figures \ref{fig:hom_2d_spectra} and \ref{fig:inhom_2d_spectra}.  %

+shape changes observed in Figures \ref{mix:fig:hom_2d_spectra} and

+\ref{mix:fig:inhom_2d_spectra}.  %

 

 \subsection{Conditional validity of the driven limit}

 

@@ -1190,7 +1191,7 @@ We have shown that the driven limit misses details of the line shape if $\Gamma_
 \approx 1$, but we have also reasoned that in certain conditions the driven limit can approximate

 the response well (see principle 1).  %

 Here we examine the line shape at delay values that demonstrate this agreement.  %  

-Fig. \ref{fig:steady_state} compares the results of our numerical simulation (third column) with

+Fig. \ref{mix:fig:steady_state} compares the results of our numerical simulation (third column) with

 the driven limit expressions for populations where $\Gamma_{11}\Delta_t=0$ (first column) or $1$

 (second column).  %

 The top and bottom rows compare the line shapes when $\left(\tau_{22^\prime},

@@ -1214,7 +1215,7 @@ with the pulse duration ($\Gamma_{11}\Delta_t=1$), which gives good agreement wi
 simulation (third column).  %

 

 When $\tau_{22^\prime}=0$ and $\tau_{21}<\Delta_t$, signals can also be approximated by driven

-signal (Fig. \ref{fig:steady_state} bottom row).  %

+signal (\autoref{mix:fig:steady_state} bottom row).  %

 Only time-orderings V and VI are relevant.  %

 The intermediate population resonance is still impulsive but it depends on

 $\omega_{2^\prime}-\omega_2$ which is not explored in our 2D frequency space.  %

diff --git a/opa/chapter.tex b/opa/chapter.tex
index db88304..00e3203 100644
--- a/opa/chapter.tex
+++ b/opa/chapter.tex
@@ -121,7 +121,7 @@ After the poweramp, the output signal and idler can be sent through appropriate
 optionally, mixed further in three subsequent mixing stages to create all of the ranges seen in

 \autoref{opa:fig:ranges}.  %

 Each of these mixing stages has only crystal angle tunability.  %

-I describe my strategy for mixer tuning in \autoref{opa:sec:mixer}.  %

+I describe my strategy for mixer tuning in \autoref{opa:sec:mixers}.  %

 

 It is important to realize that the total conversion efficiency for each output color varies wildly

 over all of the different mixing strategies.  %

@@ -180,7 +180,8 @@ I then search along that contour in \emph{intensity} space to find the motor pos
 maximum intensity for that color.  %

 Finally I fit a smooth spline through those chosen values to generate the output curve.  %

 

-A representative preamp tune procedure output image is shown in \autoref{fig:autotune_preamp}.  %

+A representative preamp tune procedure output image is shown in

+\autoref{opa:fig:autotune_preamp}.  %

 This is an automatically generated image from PyCMDS.  %

 The thick black line is the final output curve.  %

 The dark grey lines are the contours of constant color.  %

@@ -233,14 +234,14 @@ dimensions (this is especially true at the edge output colors).  %
 In the poweramp, the increased dimensionaity makes it too expensive to do a full multidimensional

 tuning procedure.  %

 Instead we emply an iterative procedure as diagrammed below.  %

-\autoref{fig:opa:poweramp_flowchart} diagrams this iterative procedure.  %

+\autoref{opa:fig:poweramp_flowchart} diagrams this iterative procedure.  %

 

 We always end the iteration(s) with C2 so that the OPA's color calibration is as good as

 possible.  %

 Typically only one iteration is required but multiple iterations may be necessary if dramatic OPA

 realignment has occurred.  %

 In total, poweramp tuning typically takes less than 1 hour. %

-Representative procedure output images for D2 (\autoref{op:fig:d2}) and C2 (\autoref{opa:fig:c2})

+Representative procedure output images for D2 (\autoref{opa:fig:d2}) and C2 (\autoref{opa:fig:c2})

 are shown.  %

 

 For the D2 figure, the lower panel shows the intensity of the data taken.  %

diff --git a/quantitative_ta/chapter.tex b/quantitative_ta/chapter.tex
index 099b606..cecf2b9 100644
--- a/quantitative_ta/chapter.tex
+++ b/quantitative_ta/chapter.tex
@@ -62,7 +62,7 @@ calibration sample.  %
 Given the eight experimental measurables ($V_\mathrm{T}$, $V_\mathrm{R}$, $V_{\Delta\mathrm{T}}$,
 $V_{\Delta\mathrm{R}}$, $V_{\mathrm{T},\,\mathrm{ref}}$, $V_{\mathrm{R},\,\mathrm{ref}}$,
 $\mathcal{T}_\mathrm{ref}$, $\mathcal{R}_\mathrm{ref}$) I can express all of the intensities in
-\autoref{eq:ta_complete} in terms of $I_0$.  %
+\autoref{qta:eqn:ta_complete} in terms of $I_0$.  %
 
 \begin{eqnarray}
 C_\mathrm{T} &=& \frac{\mathcal{T}_\mathrm{ref}}{V_{\mathrm{T},\,\mathrm{ref}}} \\
@@ -73,8 +73,8 @@ I_{\Delta\mathrm{T}} &=& I_0 C_\mathrm{T} V_{\Delta\mathrm{T}} \\
 I_{\Delta\mathrm{R}} &=& I_0 C_\mathrm{R} V_{\Delta\mathrm{R}}
 \end{eqnarray} 
 
-Wonderfully, the $I_0$ cancels when plugged back in to \autoref{eq:ta_complete}, leaving a final
-expression for $\Delta A$ that only depends on my eight measurables.  %
+Wonderfully, the $I_0$ cancels when plugged back into \autoref{qta:eqn:ta_complete}, leaving a
+final expression for $\Delta A$ that only depends on my eight measurables.  %
 
 \begin{equation}
 \Delta A = - \log_{10} \left(\frac{C_\mathrm{T}(V_\mathrm{T} + V_{\Delta\mathrm{T}}) + C_\mathrm{R}(V_\mathrm{R} + V_{\Delta\mathrm{R}})}{C_\mathrm{T} V_\mathrm{T} + C_\mathrm{R} V_\mathrm{R}}\right)