\documentclass{presentation}

\title{Development of \\ Frequency-Domain Multidimensional Spectroscopy}
\subtitle{---Beyond Two Dimensions---}
\author{Blaise Thompson}

\institute{University of Wisconsin--Madison}
\date{2018-04-23}

\begin{document}
\maketitle

\begin{frame}{Introduction to CMDS}
  \begin{columns}
    \begin{column}{0.6\textwidth}
      \includegraphics[width=\textwidth]{literature/MukamelShaul2009a_1}
      \vspace{2\baselineskip} \\
      \tiny
      Figure: \\
      Mukamel, S., Tanimura, Y. and Hamm, P. (2009).
      Coherent Multidimensional Optical Spectroscopy.
      Accounts of Chemical Research, 42(9), pp.1207-1209.
  \end{column}
  \begin{column}{0.4\textwidth}
      \includegraphics[width=\textwidth]{"literature/BrownEmilyJ1999a_1"}
      \centering
      \\
      \vspace{2\baselineskip}
      $\vec{k_{\text{sig}}} = \vec{k_a} - \vec{k_b} + \vec{k_c}$
      \vspace{2\baselineskip} \\
      \tiny \raggedright
      Figure: \\
      Brown, E., Zhang, Q. and Dantus, M. (1999). \\
      The Journal of Chemical Physics, 110(12), pp.5772-5788.
    \end{column}
  \end{columns}
\end{frame}

\begin{frame}{Introduction to CMDS: table layout}
  \adjincludegraphics[scale=0.2]{presentation/SK_PhDThesis_fsTable-Overview} \\
  \tiny
  Figure courtesy of Schuyler Kain
\end{frame}

\begin{frame}{Introduction to CMDS: microscopic picture}
  \adjincludegraphics[width=\textwidth]{"mixed_domain/simulation overview"}
\end{frame}

\begin{frame}{Diversity}
  Great diversity of experimental strategies under the ``umbrella'' of CMDS:
  \vspace{\baselineskip} \\
  Experimental geometry...
  \begin{itemize}
    \item $\vec{k_a} - \vec{k_b} + \vec{k_c}$
    \item $\vec{k_a} + \vec{k_b} + \vec{k_c}$
    \item $\vec{k_a} - \vec{k_a} + \vec{k_b} + \vec{k_c} + \vec{k_d}$
  \end{itemize}
  \vspace{\baselineskip}
  Dimensions explored...
  \begin{itemize}
    \item MIR \& visible: DOVE, TRSF
    \item fully visible: TREE, CARS
    \item frequency-frequency: 2DES/2DIR, ``Resonant-(Raman/IR)''
    \item frequency-delay: TG, TA
    \item delay-delay: 3PE, MUPPETS
  \end{itemize}
  Or 3D.. or 4D: many possibilities not yet popular enough to name  
  % SAY: based on the same basic ability to scan pulses in frequency, delay etc
\end{frame}

\begin{frame}{Pipeline}
  \adjincludegraphics[width=0.5\textwidth]{presentation/pipe}
  What does the ``pipeline'' of MR-CMDS data acquisition and processing look like in the Wright
  Group?
  \vspace{\baselineskip} \\
  How to increase data throughput and quality, while decreasing frustration of experimentalists?  %
\end{frame}

\begin{frame}{MR-CMDS development}
  [SUMMARY SLIDE FOR REMAINDER OF PRESENTATION]
\end{frame}

\section{Tunability}  % ===========================================================================

\begin{frame}{Tunability}
  \centering \huge
  Control and Calibration of \\
  Optical Parametric Amplifiers
\end{frame}

\begin{frame}{Two strategies for CMDS}
  Two strategies for collecting multidimensional spectra:
  \vspace{\baselineskip} \\
  \begin{columns}
    \begin{column}{0.4\textwidth}
      Time Domain
      \begin{itemize}
        \item broadband pulses
        \item resolve spectra interferometrically
        \item fast (even single shot)
        \item robust
      \end{itemize} 
    \end{column}
    \begin{column}{0.4\textwidth}
      Frequency Domain
      \begin{itemize}
        \item narrowband pulses
        \item resolve spectra by tuning OPAs directly
        \item slow (lots of motor motion)
        \item fragile
      \end{itemize}
    \end{column}
  \end{columns}
\end{frame}

\begin{frame}{Two strategies for CMDS}
  \begin{columns}
    \begin{column}{0.5\textwidth}
      Time Domain
      \includegraphics[width=\textwidth]{"literature/SinghAkshay2014a_2"}
      \tiny
      Figure: \\
      Singh, A., Moody, G., Wu, S., Wu, Y., Ghimire, N., Yan, J., Mandrus, D., Xu, X. and Li, X.
      (2014).
      Coherent Electronic Coupling in Atomically Thin MoSe$_2$. Physical Review Letters, 112(21).
    \end{column}
    \begin{column}{0.5\textwidth}
      Frequency Domain
      \adjincludegraphics[width=\textwidth]{presentation/singh_czech}
      More \hl{bandwidth}.
      Crucial for electronic states, band structure.
    \end{column}
  \end{columns}
\end{frame}

\begin{frame}{Bandwidth}
  A lot more bandwidth... through the usage of OPAs
  \adjincludegraphics[width=\textwidth]{opa/OPA_ranges}
\end{frame}

\begin{frame}{TOPAS-C}
  One of four models of OPAs used within the Wright Group.
  \includegraphics[width=\textwidth]{opa/TOPAS-C}
  Two ``stages'', each with two motorized optics.
  % SAY: crystal angle, temporal and spatial overlap
\end{frame}

\begin{frame}{Automation}
  \begin{columns}
    \begin{column}{0.5\textwidth}
      \adjincludegraphics[width=\textwidth]{opa/autotune_preamp}
    \end{column}
    \begin{column}{0.5\textwidth}
      Fully automated OPA tuning
      \begin{itemize}
        \item less than 1 hour per OPA
        \item can be scheduled for odd times
        \item high quality from global analysis 
        \item reproducible
        \item unambiguous representations automatically generated
      \end{itemize}
      \vspace{\baselineskip}
      Other calibration steps also automated.
    \end{column}
  \end{columns}
\end{frame}

\section{Acquisition}  % ==========================================================================

\begin{frame}{Acquisition}
  \centering \huge
  Control of the MR-CMDS \\
  Instrument
\end{frame}

\begin{frame}{The instrument}
  Many kinds of component hardware
  \begin{itemize}
    \item monochromators
    \item delay stages
    \item filters
    \item OPAs
  \end{itemize}
  $\sim10$ settable devices, $\sim25$ motors, multiple detectors. \\
  \vphantom{M} \\
  \hl{Coordination} problem.
\end{frame}

\begin{frame}{Acquisition}
  PyCMDS---unified software for controlling hardware and collecting data.
  \adjincludegraphics[width=\textwidth]{acquisition/screenshots/000}
\end{frame}

\begin{frame}{Acquisition}
  Capabilities of PyCMDS:
  \begin{itemize}
    \item reconfigures itself based on available hardware (modularity)
    \item multithreaded (up to 2x speed enhancement)
    \item queued acquisitions
      \begin{itemize}
        \item long scans, short window of calibration---large duty cycle needed
        \item shortly after implementation, two weeks of data collection yielded as many pixels as
          the previous three years
      \end{itemize}
    \item extensibility
      \begin{itemize}
        \item easy to add new hardware, new sensors, and new acquisition strategies
        \item typical addition $\sim100$ lines of new Python code
      \end{itemize}
  \end{itemize}
\end{frame}

\begin{frame}{Extensibility}
  \begin{columns}
    \begin{column}{0.25\textwidth}
      \adjincludegraphics[width=\textwidth]{presentation/hardware}
    \end{column}
    \begin{column}{0.7\textwidth}
      Easy to add new hardware to PyCMDS
      \begin{itemize}
        \item In 2016, a new OPA was added to the picosecond system in one day.
        \item In 2017, we added multiple delay stages to the femtosecond system. Implementation
          took between one and four hours.
      \end{itemize}
      Once added, new hardware is immediately available for scanning in a multidimensional space
      with other hardware---no additional programming needed!
    \end{column}
  \end{columns}
\end{frame}

\section{Measurement enhancements}  % =============================================================

\begin{frame}{Artifacts}
  \centering \huge
  Measurement enhancements
\end{frame}

\begin{frame}{Digital processing}
  \large
  boxcar averager \hfill digitize immediately
  \begin{columns}
    \begin{column}{0.5\textwidth}
      \adjincludegraphics[width=\textwidth]{presentation/IMG_20180422_145632}
    \end{column}
    \begin{column}{0.5\textwidth}
      \adjincludegraphics[width=\textwidth]{presentation/measure_digitize}
    \end{column}
  \end{columns}
  \vphantom{M} \\
  More flexibility:
  \begin{itemize}
    \item shot-level statistics
    \item more complex multi-pulse sequences allowed for
  \end{itemize}
  plus... no need to wait for the boxcar averager to settle (speed up of $\sim3\times$)  
\end{frame}

\begin{frame}{Dual chopping}
  \begin{columns}
    \begin{column}{0.5\textwidth}
      \adjincludegraphics[width=\textwidth]{presentation/IMG_20180422_145757}
    \end{column}
    \begin{column}{0.5\textwidth}
      \begin{tabular}{ r | c | c | c | c }
        & A       & B          & C          & D                       \\
        signal    &            &            & \checkmark &            \\
        scatter 1 &            & \checkmark & \checkmark &            \\
        scatter 2 &            &            & \checkmark & \checkmark \\
        other     & \checkmark & \checkmark & \checkmark & \checkmark
      \end{tabular}
    \end{column}
  \end{columns}
  \centering
  \vphantom{M}
  $\mathsf{I_{signal} = A - B + C - D}$
  \begin{itemize}
    \item{no scatter}
    \item{no two-beam processes}
    \item{no voltage offset or room lights}
  \end{itemize}
\end{frame}

\begin{frame}{Digital processing}
  \adjincludegraphics[height=3in]{MX2/11}
\end{frame}

\section{Processing}  % ===========================================================================

\begin{frame}{Processing}
  \centering \huge
  Data processing
\end{frame}

\begin{frame}{Processing}
  \begin{columns}
    \begin{column}{0.6\textwidth}
      Great! We have \emph{lots} of CMDS data. \\
      Now what? \\
      \vphantom{M} \\
      Working with multidimensional data is hard...
      \begin{itemize}
        \item storage
        \item visualization
        \item post-processing
        \item fitting, modeling
      \end{itemize}
      and the dimensions are always changing! \\
      \vphantom{M} \\
      WrightTools---software to process CMDS.
    \end{column}
    \begin{column}{0.4\textwidth}
      \adjincludegraphics[width=\textwidth]{presentation/TOC}
    \end{column}
  \end{columns}
\end{frame}

\subsection{Universal format}  % ------------------------------------------------------------------

\begin{frame}{Universal format}
  WrightTools defines a \emph{universal file format} for CMDS.
  \begin{itemize}
    \item store multiple multidimensional arrays
    \item metadata
  \end{itemize}
  Import data from a variety of sources.
  \begin{itemize}
    \item previous Wright Group acquisition software
    \item commercial instruments (JASCO, Shimadzu, Ocean Optics)
  \end{itemize}
\end{frame}

\subsection{Flexible data model}  % ---------------------------------------------------------------

\begin{frame}{Flexible data model}
  Flexibility to transform into any desired ``projection'' on component variables.
  \adjincludegraphics[width=\textwidth]{processing/fringes_transform}
  % mention: including expressions
\end{frame}

\subsection{Integrations}  % -----------------------------------------------------------------------

\begin{frame}{Integrations}
  \adjincludegraphics[height=3in]{PbSe_global_analysis/movies_fitted}
\end{frame}

\section{Conclusion}  % ===========================================================================

\begin{frame}{Conclusion}
  [CONCLUSION]
\end{frame}

\begin{frame}{Acknowledgments}
  \begin{columns}
    \begin{column}{0.5\textwidth}
      Wright Group
      \begin{itemize}
        \item Kyle Sunden
        \item Natalia Spitha
        \item Darien Morrow
        \item Jonathan Handali
        \item Nathan Neff-Mallon
        \item Kyle Czech         
        \item Dan Kohler
        \item Erin Boyle
        \item Paul Hebert
        \item Skye Kain
        \item John
        \item (and more...)
      \end{itemize}
    \end{column}
    \begin{column}{0.5\textwidth}
      Committee
      \begin{itemize}
        \item Kyoung-Shin Choi
        \item Randall Goldsmith
        \item Tim Bertram
      \end{itemize}
      \phantom{M}
      UW-Madison Chemistry Department
      \begin{itemize}
        \item Rob McClain
        \item Pam Doolittle
        \item Bill Goebel
        \item Steve Myers
      \end{itemize}
      \phantom{M}
      You, the audience!
      \hl{Questions?}
    \end{column}
  \end{columns}
\end{frame}
  
\section{Supplement}  % ===========================================================================

\begin{frame}{Tuning}
  % TODO: curve plot?
  Tuning curves---recorded correspondence between motor positions and output color.
  \vspace{\baselineskip} \\
  Exquisite sensitivity to alignment and lab conditions---tuning required roughly once a week.
  \vspace{\baselineskip} \\
  Manual tuning is difficult...
  \begin{itemize}
    \item high dimensional motor space
    \item difficult to asses overall quality
    \item several hours of work per OPA (sometimes, an entire day for one OPA)
  \end{itemize}
\end{frame}

\begin{frame}{Preamp}
  \includegraphics[width=\textwidth]{opa/preamp}
\end{frame}

\begin{frame}{Modular hardware model}
  \adjincludegraphics[scale=0.25]{acquisition/hardware_inheritance}
\end{frame}

\begin{frame}{Modular sensor model}
  Can have as many sensors as needed.
  \vspace{\baselineskip} \\
  Each sensor contributes one or more channels.
  \vspace{\baselineskip} \\
  Sensors with size contribute new variables (dimensions).
\end{frame}

\begin{frame}{Domains of CMDS}
  CMDS can be collected in two domains:
  \begin{itemize}
    \item time domain
    \item frequency domain
  \end{itemize}
\end{frame}

\begin{frame}{Time domain}
  Multiple broadband pulses are scanned in \emph{time} to collect a multidimensional interferogram
  (analogous to FTIR, NMR).
  \vspace{\baselineskip} \\
  A local oscillator must be used to measure the \emph{phase} of the output.
  \vspace{\baselineskip} \\
  This technique is...
  \begin{itemize}
    \item fast (even single shot)
    \item robust
  \end{itemize}
  pulse shapers have made time-domain CMDS (2DIR) almost routine.
\end{frame}

\begin{frame}{Frequency domain}
  In the Wright Group, we focus on \emph{frequency} domain ``Multi-Resonant'' (MR)-CMDS.
  \vspace{\baselineskip} \\
  Automated Optical Parametric Amplifiers (OPAs) are used to produce relatively narrow-band pulses.
  Multidimensional spectra are collected ``directly'' by scanning OPAs against each-other.
  \vspace{\baselineskip} \\
  This strategy is...
  \begin{itemize}
    \item slow (must directly visit each pixel)
    \item fragile (many crucial moving pieces)
  \end{itemize}
  but! It is incredibly flexible.  
\end{frame}

\begin{frame}{Selection rules}
  MR-CMDS can easily collect data without an external local oscillator.
  \vspace{\baselineskip} \\
  This means... [BOYLE]
\end{frame}

\begin{frame}{Central loop}
  At its core, PyCMDS does something very simple...
  \vspace{\baselineskip} \\ 
  Set, wait, read, wait, repeat.  % TODO: better figure
  \vspace{\baselineskip} \\
  Everything is multi-threaded (simultaneous motion, simultaneous read).
  \begin{itemize}
    \item decrease scan time by up to $\sim2\times$, more for complex experiments
  \end{itemize}
\end{frame}

\end{document}