Checkout the code using the following command:

git clone --recursive https://github.com/star-bnl/star-vertex-eval.git

Compile and build the library:

cd star-vertex-eval/
mkdir build && cd build/
cmake -D CMAKE_INSTALL_PREFIX=./ ../
make install

The above commands should build libstar-vertex-eval.so in the build directory. Now we can load it in ROOT and call MuMcPrVKFV2012() to create a ROOT file (e.g. my_output_file.root) with support histograms used to calculate the vertex finding efficiency. To produce and the efficiency plots one can follow up with a call to VxEff() as in the snipped below:

root -l ../load_libs.C
root [0] MuMcPrVKFV2012(<num_events>, "path/to/*.MuDst.root", "my_output_file.root");
root [1] std::vector<std::string> myKeys, myFiles; myKeys.push_back("DFLT"); myFiles.push_back("my_output_file.root");
root [2] VxEff(myKeys, myFiles);

To create histograms from muDst files one can do from the same location:

root -l ../load_libs.C
root [0] VertexRank.C(<num_events>, "path/to/*.MuDst.root", "my_output_file")

Checkout the code using one of the following commands:

git clone [email protected]:star-bnl/star-vertex-eval.git       # If you have an account on github.com
git clone https://github.com/star-bnl/star-vertex-eval.git   # otherwise.

Get the code dependencies by issuing the following commands:

cd star-vertex-eval/
git submodule update --init

Now create a directory from where condor jobs will be submitted and run the script as:

mkdir submit_jpsi && cd submit_jpsi
/path/to/star-vertex-eval/supple/submit_jobs_embedding_jpsi.sh 200

If the package was not checked out in the default location of ${HOME}/star-vertex-eval/ then you should specify the correct location as:

SOURCE_DIR=/path/to/star-vertex-eval/ \
OUTPUT_DIR=/tmp/jpsi_embed \
/path/to/star-vertex-eval/supple/submit_jobs_embedding_jpsi.sh 200

There is a number of other script parameters which can be modified if the default values are not appropriate. Search for "NAMED ARGUMENTS" to find these values.

1.- The macro VertexRank.C will read the MuDst sample and create a tree with some vertex information. To run (e.g. 100 events and output file outvertex.root):

$root -l -b -q lMuDst.C 'VertexRank.C(100, "path/to/*.MuDst.root", "outvertex")'

2.- Run TMVA to classify signal and background. A separate set of signal and background must be given, with a define set of variables for the training. Also need to specify the classifier. To run (e.g. with classifiers Fisher and Likelihood):

$root TMVAClassification.C\(\"Fisher,Likelihood\"\)

3.- Apply the results of the classification with ApplyClassifier.C. The output will be the same tree for step (1) but with a new branch with the classifier response. Run:

$root ApplyClassifier.C