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  Goals:  1. Be introduced to nuclear radiation and the detection 
             of radiation using common nuclear physics research tools.
          2. Acquire gamma emission data for various gamma-ray sources.
          3. Perform a simple analysis of the acquired data utilizing the 
             ROOT data analysis package (written in C++) which is used by 
             the nuclear and high energy physics community.
          4. Interpret the data to extract the identity of a unknown
             gamma-ray source, and present the analysis to convince an 
             audience (our class) of your result.
          5. Cover additional topics:  storing multiple experimental quantities
             in a convenient form, imposing constraints (a.k.a., cuts) on 
             experimental quantities to investigate relationships, writing code
             that makes the investigation efficient.
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    Session I: Gamma-ray Spectroscopy
        I. Discussion of nuclear radiation
          A. Definition of radiation: the process in which energy is emitted as 
             particles or waves.
          B. How would one detect the presence charged particles?
             What about a uncharged particle such as a gamma ray or neutron?
             How would one determine the energy of a particle?
       II. Types of nuclear radiation
          A. Charged particles: alpha, beta
          B. Neutral particles: gamma, neutron
      III. Sources of nuclear radiation
          A. Nuclear reactions: radiative capture, fusion, fission
          B. Radioactive decay
             Definition: process in which an unstable nucleus loses energy 
             by emitting radiation in the form of particles or waves.
            1. Alpha decay
            2. Beta decay
            3. Gamma emission and annihilation radiation
            --
            electron-volt (eV) - a unit of energy, equal to the energy acquired 
            by an electron accelerating through a potential difference of one volt 
            (1 eV = 1.602e-19 J).
       IV. Methods of particle detection (det_properties.pdf)
          A. Detection of charged particles
          B. Detection of uncharged particles
        V. Electronics and signal processing (det_electronics.png)
          A. NIM standard
          B. Analog and digital logic signals
       VI. Data acquisition (DAQ)
          A. Stingray oscilloscope
          B. LabVIEW VI (MCA)
      VII. Data collection and on-line analysis
          A. Calibration using a known source(s)
          B. Take data run(s) with unknown source

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   Session II: Coding In C++ / Spectrum Analysis Using ROOT
        I. C++
          A. Object-oriented programming (OOP)
            1. Vending machine analogy for "object"
            2. Example class in ROOT: TCanvas
               http://root.cern.ch/root/html/ClassIndex.html
          B. Pointers in C++
            1. new and delete
          C. Text file input and output
            1. ifstream
            2. ofstream
       II. ROOT - An Object Oriented Data Analysis Framework
          A. From first paper about ROOT: "The ROOT system is an object-oriented
             framework for large scale data analysis.  ROOT written in C++, 
             contains, among others, an efficient hierarchical OO database, a C++
             interpreter, advanced statistical analysis (multi-dimensional 
             histograming, fitting, minimization, cluster finding algorithms) and 
             visualization tools.  The user interacts with ROOT via a graphical 
             user interface, the command line, or batch scripts.  The command and 
             scripting language is C++ (using the interpreter), and large scripts 
             can be compiled and dynamically linked in.  The OO database design 
             has been optimized for parallel access (reading as well as writing) 
             by multiple processes."
          B. Walk-through of plotHist.C
            1. Drawing a histogram on a canvas (TH1D*, TCanvas*)
            2. Saving objects to ROOT files (TFile*)
          C. Fitting example: fitExample.C
            1. Defining a fitting function (TF1*)
            2. Fitting a histogram with the function
            2. Extracting the results of the fit
      III. Analysis project
          A. Determine identity of the unknown source.
          B. Fit any identified peaks with a Gaussian function, and
             give an estimate (the error) on how well the centroid 
             is determined.
          C. Produce a figure displaying a calibrated spectrum of the
             of the unknown source with the relevent peaks labeled by the
             energy and the error in the energy. (Use the class 
             TLatex for the labels.)

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  Session III:

trees, cuts, graphs with errors, drawings