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# MASK: Monte cArlo Simulation of Kinematics
MASK is a Monte Carlo simulation of reaction kinematics intended for use with the Super-Enge Split-pole Spectrograph (SESPS) at Florida State University.
MASK is capable of simulating multi-step reaction-decay sequences, however in a purely kinematic sense, as it currently has no quantum mechanical input (this
is planned to be added in the next version). It is also capable of testing detector efficiency; this version contains the methods necessary to simulate the efficiency
of a reaction into the Silicion Array for Branching Ratio Detectors (SABRE) as well as ANASEN.
MASK is a Monte Carlo simulation of reaction kinematics for use detector systems at Florida State University.
MASK is capable of simulating multi-step kinematic reaction-decay sequences, storing data in a lightweight binary format, after which the kinematic data can be fed to a detector geometry for efficiency testing. Currently geometries for ANASEN and SABRE are included in the code.
## Building MASK
Dowload the repository from github. The code is to be built using Premake5, an open source and free project building software. To build on Linux and MacOSX run
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`make`
to build the program. To build on Windows run
`premake5 vs20xx`
where xx should be replaced with your version of Visual Studio, and then build as a normal Visual Studio project. For more documentation see the Premake wiki.
NOTE: For Windows, ROOT is linked using the global envrionment variable ROOTSYS. If you do not have ROOTSYS set, Premake will not be able to properly link and include all of the ROOT libraries.
where xx should be replaced with your version of Visual Studio, and then build as a normal Visual Studio project. For more documentation see the Premake wiki. By default `make` runs in Debug mode. For release mode use `make config=release`
### Building RootPlot
One of the executables, RootPlot, requires linking against external libraries from the ROOT cern analysis package. Path to the necessary header files and libraries must be set by the user on a machine-by-machine basis in the premake5.lua file. Set the ROOTIncludepath and ROOTLibpath to match your install. An easy way to check for the paths on a unix system is through the root-config tool. root-config --cflags has the include path (the path after -I) and root-config --glibs has the lib path (after -L). If you do not have ROOT installed, you will not be able to compile RootPlot, and you will not be able to use the generic `make` command, which will try to build all executables. Instead use `make Mask` or `make DetectEff` (or `make config=release Mask`, etc).
## Running MASK
By default MASK is capable of simulating reactions of up to three steps. Here is a brief outline of each type:
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For decays, a specific angular distribution can be given as input as a text file with values of coefficiencts of a Legendre polynomial series. Examples can be found in the `./etc` directory, including an isotropic case. It is assumed that the decays in the center-of-mass frame are isotropic in phi (i.e. m=0). Decay1 corresponds to the first decay, if there are multiple steps, Decay2 to the second. If there are no decays, these parameters are not used (or if only one decay, Decay2_AngularMomentum is not used). The input file requires that the user include target information, which will be used to calculate energy loss for all of the reactants and reaction products. The target can contain layers, and each layer can be composed of a compound of elements with a given stoichiometry. If the user wishes to not include energy loss in the kinematics, simply give all target layers a thickness of 0. Note that more layers and more thickness = more time spent calculating energy loss. These energy loss methods are only applicable for solid targets, and should not be applied to gas or liquid targets. Energy loss calculations have a stated uncertainty of approximately five percent.
The default MASK program includes a calculation of SABRE efficiency, whose methods are contained in the SabreEfficiency and SabreDetector classes. This can be disabled by modifying the main.cpp file appropriately. It also has the classes for calculating ANASEN efficiency. More detector classes can be added following the format outlined by these two examples.
In the input file the user also has the option to select to save the ROOT tree of the simulated data and the default plots. The options are yes or no. Yes saves them, no doesn't.
To choose which detector scheme is run, modify the main function in DetectorEfficiency.cpp. The included geometries also have options to do an internal geometry consistency check and print out coordinates for drawing the detector arrays.
To run MASK simply do the following from the MASK directory:
`./bin/mask input.txt`
`./bin/Mask input.txt`
Input.txt can be replaced by any text file with the correct format.
## Requirements
MASK requires that ROOT is installed for data writting and visualization, as well as for random number generation. Testing has been done only on ROOT 6. Mileage on all other ROOT versions will vary.
To run DetEff use the format
Currently supports MacOSX and Linux. Windows is in a beta stage.
`./bin/DetEff <kinematics_datafile> <new_detection_datafile> <new_detection_statsfile>`
where the detection datafile contains all of the kinematics data as well as information about which particles are detected (this is in the mask file format) and the statsfile is a text file containing efficiency statistics.
RootPlot is run as
`./bin/RootPlot <datafile> <outputfile>`
where the datafile can be either the datafile from Mask or the datafile from DetEff. The outputfile is saved in the ROOT file format.
PyPlotter is run as
`./bin/PyPlotter <datafile> <outputfile>`
where again datafile can come from either Mask or DetEff. The outputfile is saved in the Python pickle file format. Pickled files can be reopened using the PyPlotViewer script run as
`./bin/PyPlotViewer <picklefile>`
## Requirements
MASK, the kinematics simulation, and DetEff, the detection efficiency simulation, require no external dependancies. The RootPlot plotting tool requires the ROOT cern data analysis package, and the PyPlotter tool requires Python3 and the matplotlib and numpy packages.