The STAR experiment

Once the variables were created, as mentioned above, you must make sure all your variables are present in all the trees you are adding to the factory. My solution to this was to make the trees friends via the TTree::AddFriend() function. If not you will get this error

Error in <TTreeFormula::Compile>: Bad numerical expression :
> "myvariable"
> --- <FATAL> DataSetFactory : Expression myvariable could not
> be resolved to a valid formula.
> ***> abort program execution

Where "myvariable" is the variable you are trying to add to the factory. Once that is done you can go to /star/u/rsalinas and execute the following:

• make -f makefile.D
• make -f makfefile.C
  • This creates the binaries necessary for the embedding 
• ./RUN.sh
  • This runs the embedding binaries for the particle and detector levels
• The output files should be called "outfilep.root" & "outfiled.root" 

Once that is completed, you can run the TMVA regression file via the command:

• root -l TMVARegression.C\(\"MLP\"\)
  • You can add more methods by separating them w/ commas \(\"LD,MLP,...\"\)
  • Running this is the training aspect of the neural net.

TMVA has a nice GUI that displays how the training went and here are a few results:

• Re-scaled input variable and regression target 

• Regression Output 
  • The weight per tree is 1
  • 1000 training events and 4380 testing events
  • Each neuron had an activation function of tanh
• From this simple example, you can see that there is little disagreement between the regression output and the true value used for testing

• The norm also shows small deviation

While currently it seems to work well, this is a very simple example and will need further study and see how it compares with the unfolding package RooUnfold that is also native to ROOT. This method still yet has to be applied to actual data and will be given as an update to this blog entry.